# Differential expression on Kallisto data 

# Preliminary samples - 2016 dataset

# Packages and dependence
packageCheckClassic <- function(x){
  # 
  for( i in x ){
    if( ! require( i , character.only = TRUE ) ){
      install.packages( i , dependencies = TRUE )
      require( i , character.only = TRUE )
    }
  }
}

packageCheckClassic(c('DESeq2','adegenet','vsn','devtools','BiocManager','ggplot2','ggrepel','markdown','pheatmap','RColorBrewer','genefilter','gplots','vegan','dplyr','limma'))
## Le chargement a nécessité le package : DESeq2
## Le chargement a nécessité le package : S4Vectors
## Warning: le package 'S4Vectors' a été compilé avec la version R 4.1.3
## Le chargement a nécessité le package : stats4
## Le chargement a nécessité le package : BiocGenerics
## 
## Attachement du package : 'BiocGenerics'
## Les objets suivants sont masqués depuis 'package:stats':
## 
##     IQR, mad, sd, var, xtabs
## Les objets suivants sont masqués depuis 'package:base':
## 
##     anyDuplicated, append, as.data.frame, basename, cbind, colnames,
##     dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
##     grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
##     order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
##     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
##     union, unique, unsplit, which.max, which.min
## 
## Attachement du package : 'S4Vectors'
## Les objets suivants sont masqués depuis 'package:base':
## 
##     expand.grid, I, unname
## Le chargement a nécessité le package : IRanges
## Le chargement a nécessité le package : GenomicRanges
## Le chargement a nécessité le package : GenomeInfoDb
## Le chargement a nécessité le package : SummarizedExperiment
## Le chargement a nécessité le package : MatrixGenerics
## Le chargement a nécessité le package : matrixStats
## 
## Attachement du package : 'MatrixGenerics'
## Les objets suivants sont masqués depuis 'package:matrixStats':
## 
##     colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
##     colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
##     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
##     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
##     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
##     colWeightedMeans, colWeightedMedians, colWeightedSds,
##     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
##     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
##     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
##     rowWeightedSds, rowWeightedVars
## Le chargement a nécessité le package : Biobase
## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.
## 
## Attachement du package : 'Biobase'
## L'objet suivant est masqué depuis 'package:MatrixGenerics':
## 
##     rowMedians
## Les objets suivants sont masqués depuis 'package:matrixStats':
## 
##     anyMissing, rowMedians
## Le chargement a nécessité le package : adegenet
## Le chargement a nécessité le package : ade4
## 
## Attachement du package : 'ade4'
## L'objet suivant est masqué depuis 'package:GenomicRanges':
## 
##     score
## L'objet suivant est masqué depuis 'package:BiocGenerics':
## 
##     score
## 
##    /// adegenet 2.1.10 is loaded ////////////
## 
##    > overview: '?adegenet'
##    > tutorials/doc/questions: 'adegenetWeb()' 
##    > bug reports/feature requests: adegenetIssues()
## Le chargement a nécessité le package : vsn
## Le chargement a nécessité le package : devtools
## Le chargement a nécessité le package : usethis
## Le chargement a nécessité le package : BiocManager
## Bioconductor version '3.14' is out-of-date; the current release version '3.16'
##   is available with R version '4.2'; see https://bioconductor.org/install
## 
## Attachement du package : 'BiocManager'
## L'objet suivant est masqué depuis 'package:devtools':
## 
##     install
## Le chargement a nécessité le package : ggplot2
## Le chargement a nécessité le package : ggrepel
## Le chargement a nécessité le package : markdown
## Le chargement a nécessité le package : pheatmap
## Le chargement a nécessité le package : RColorBrewer
## Le chargement a nécessité le package : genefilter
## 
## Attachement du package : 'genefilter'
## Les objets suivants sont masqués depuis 'package:MatrixGenerics':
## 
##     rowSds, rowVars
## Les objets suivants sont masqués depuis 'package:matrixStats':
## 
##     rowSds, rowVars
## Le chargement a nécessité le package : gplots
## 
## Attachement du package : 'gplots'
## L'objet suivant est masqué depuis 'package:IRanges':
## 
##     space
## L'objet suivant est masqué depuis 'package:S4Vectors':
## 
##     space
## L'objet suivant est masqué depuis 'package:stats':
## 
##     lowess
## Le chargement a nécessité le package : vegan
## Le chargement a nécessité le package : permute
## 
## Attachement du package : 'permute'
## L'objet suivant est masqué depuis 'package:devtools':
## 
##     check
## Le chargement a nécessité le package : lattice
## This is vegan 2.6-4
## Le chargement a nécessité le package : dplyr
## 
## Attachement du package : 'dplyr'
## L'objet suivant est masqué depuis 'package:Biobase':
## 
##     combine
## L'objet suivant est masqué depuis 'package:matrixStats':
## 
##     count
## Les objets suivants sont masqués depuis 'package:GenomicRanges':
## 
##     intersect, setdiff, union
## L'objet suivant est masqué depuis 'package:GenomeInfoDb':
## 
##     intersect
## Les objets suivants sont masqués depuis 'package:IRanges':
## 
##     collapse, desc, intersect, setdiff, slice, union
## Les objets suivants sont masqués depuis 'package:S4Vectors':
## 
##     first, intersect, rename, setdiff, setequal, union
## Les objets suivants sont masqués depuis 'package:BiocGenerics':
## 
##     combine, intersect, setdiff, union
## Les objets suivants sont masqués depuis 'package:stats':
## 
##     filter, lag
## Les objets suivants sont masqués depuis 'package:base':
## 
##     intersect, setdiff, setequal, union
## Le chargement a nécessité le package : limma
## Warning: le package 'limma' a été compilé avec la version R 4.1.3
## 
## Attachement du package : 'limma'
## L'objet suivant est masqué depuis 'package:DESeq2':
## 
##     plotMA
## L'objet suivant est masqué depuis 'package:BiocGenerics':
## 
##     plotMA
#BiocManager::install('tximport', force = TRUE)
#BiocManager::install('apeglm')
#BiocManager::install('ashr')
#BiocManager::install("EnhancedVolcano")
#BiocManager::install("arrayQualityMetrics")
if (!require(devtools)) install.packages("devtools")
devtools::install_github("yanlinlin82/ggvenn")
## Skipping install of 'ggvenn' from a github remote, the SHA1 (25fd3b55) has not changed since last install.
##   Use `force = TRUE` to force installation
library("adegenet")
library('ggvenn')
## Le chargement a nécessité le package : grid
library('tximport')
library('apeglm')
library('ashr')
library('EnhancedVolcano')
## Registered S3 methods overwritten by 'ggalt':
##   method                  from   
##   grid.draw.absoluteGrob  ggplot2
##   grobHeight.absoluteGrob ggplot2
##   grobWidth.absoluteGrob  ggplot2
##   grobX.absoluteGrob      ggplot2
##   grobY.absoluteGrob      ggplot2
library('BiocManager')
source_url("https://raw.githubusercontent.com/obigriffith/biostar-tutorials/master/Heatmaps/heatmap.3.R")
## ℹ SHA-1 hash of file is "015fc0457e61e3e93a903e69a24d96d2dac7b9fb"
# Working environment and data loading
scriptPath<-dirname(rstudioapi::getSourceEditorContext()$path)
setwd(scriptPath)
#candidateGenes<-read.csv('candidateGenes.csv',header=T,sep=',')
samplesSaccharina<-read.table('saccharinaDesignMulti.txt',header=T)
samplesHedophylum<-read.table('hedophylumDesignMulti.txt',header=T)
dataPath<-'/Users/mmeynadier/Documents/kelpProject/kallistoOutput'
outputPath<-'/Users/mmeynadier/Documents/kelpProject/DESeq2Output'
#setwd(dataPath)

# DDS object

# If data from kallisto
tx2geneSaccharina<-read.table('Saccharina_tx2gene',header=T)
tx2geneHedophylum<-read.table('Hedophylum_tx2gene',header=T)


# 
# # Data importation - txImport
setwd(dataPath)
filesSaccharina<-paste0(samplesSaccharina$sample)
txiSaccharina<-tximport(files = filesSaccharina,type='kallisto',tx2gene = tx2geneSaccharina)
## Note: importing `abundance.h5` is typically faster than `abundance.tsv`
## reading in files with read_tsv
## 1 2 3 4 5 6 7 8 
## transcripts missing from tx2gene: 8998
## summarizing abundance
## summarizing counts
## summarizing length
filesHedophylum<-paste0(samplesHedophylum$sample)
txiHedophylum<-tximport(files = filesHedophylum,type='kallisto',tx2gene = tx2geneHedophylum)
## Note: importing `abundance.h5` is typically faster than `abundance.tsv`
## reading in files with read_tsv
## 1 2 3 4 5 6 7 8 9 
## transcripts missing from tx2gene: 9442
## summarizing abundance
## summarizing counts
## summarizing length
ddsSaccharina<-DESeqDataSetFromTximport(txiSaccharina,colData=samplesSaccharina,design= ~treatment + mesocosm)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## using counts and average transcript lengths from tximport
ddsHedophylum<-DESeqDataSetFromTximport(txiHedophylum,colData=samplesHedophylum,design= ~treatment + mesocosm)
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## using counts and average transcript lengths from tximport
# pre-filtering
keepSaccharina <- rowSums(counts(ddsSaccharina)) >= 10 
ddsSaccharina <- ddsSaccharina[keepSaccharina,]
keepHedophylum <- rowSums(counts(ddsHedophylum)) >= 10 
ddsHedophylum <- ddsHedophylum[keepHedophylum,]

# Differential expression analysis
ddsSaccharina<-DESeq(ddsSaccharina)
## estimating size factors
## using 'avgTxLength' from assays(dds), correcting for library size
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
cbind(resultsNames(ddsSaccharina))
##      [,1]               
## [1,] "Intercept"        
## [2,] "treatment_T1_vs_C"
## [3,] "treatment_T2_vs_C"
## [4,] "treatment_T3_vs_C"
## [5,] "mesocosm_M1_vs_M0"
## [6,] "mesocosm_M2_vs_M0"
ddsHedophylum<-DESeq(ddsHedophylum)
## estimating size factors
## using 'avgTxLength' from assays(dds), correcting for library size
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
cbind(resultsNames(ddsHedophylum))
##      [,1]               
## [1,] "Intercept"        
## [2,] "treatment_T1_vs_C"
## [3,] "treatment_T2_vs_C"
## [4,] "treatment_T3_vs_C"
## [5,] "mesocosm_M1_vs_M0"
## [6,] "mesocosm_M2_vs_M0"
# Exploring the results - Saccharina

S_C_vs_T1 <- results(ddsSaccharina,contrast=c("treatment", "C", "T1"))
S_C_vs_T2 <- results(ddsSaccharina,contrast=c("treatment", "C", "T2"))
S_C_vs_T3 <- results(ddsSaccharina,contrast=c("treatment", "C", "T3"))
S_T1_vs_T2 <- results(ddsSaccharina,contrast=c("treatment", "T1", "T2"))
S_T1_vs_T3 <- results(ddsSaccharina,contrast=c("treatment", "T1", "T3"))
S_T2_vs_T3 <- results(ddsSaccharina,contrast=c("treatment", "T2", "T3"))

DESeq2::plotMA(S_C_vs_T1,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T1")

DESeq2::plotMA(S_C_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T2")

DESeq2::plotMA(S_C_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T3")

DESeq2::plotMA(S_T1_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T2")

DESeq2::plotMA(S_T1_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T3")

DESeq2::plotMA(S_T2_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT2 vs T3")

vsdSaccharina <- vst(ddsSaccharina, blind=T)

meanSdPlot(assay(vsdSaccharina))

ntd <- normTransform(ddsSaccharina)
meanSdPlot(assay(ntd))

select <- order(rowMeans(counts(ddsSaccharina,normalized=TRUE)),
                decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsSaccharina)[,c("treatment","mesocosm")])
pheatmap(assay(vsdSaccharina)[select,], cluster_rows=FALSE, show_rownames=F,
         cluster_cols=FALSE, annotation_col=df)

pcaData <- plotPCA(vsdSaccharina, intgroup=c("treatment", "mesocosm"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(PC1, PC2, color=treatment, shape=mesocosm)) +
  geom_point(size=3) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed()

sampleDists <- dist(t(assay(vsdSaccharina)))
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsdSaccharina$treatment, vsdSaccharina$mesocosm, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         col=colors)

count_tab_assay <- assay(vsdSaccharina)
dist_tab_assay <- dist(t(count_tab_assay),method="euclidian")
adonis(data=samplesSaccharina,dist_tab_assay ~ treatment + mesocosm, method="euclidian")
## 'adonis' will be deprecated: use 'adonis2' instead
## $aov.tab
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)
## treatment  3     97266   32422 0.92564 0.39825  0.654
## mesocosm   2     76913   38456 1.09792 0.31492  0.326
## Residuals  2     70053   35027         0.28683       
## Total      7    244232                 1.00000       
## 
## $call
## adonis(formula = dist_tab_assay ~ treatment + mesocosm, data = samplesSaccharina, 
##     method = "euclidian")
## 
## $coefficients
## NULL
## 
## $coef.sites
##                     [,1]       [,2]        [,3]        [,4]       [,5]
## (Intercept)  280.7711645  288.85452 225.2320469  165.182879 207.888057
## treatment1  -103.7689116 -116.40654  56.5620319   10.281629  52.238253
## treatment2    42.0223555   26.34908  37.1787404   -0.305203 -64.886478
## treatment3     2.2744865   63.95154  -4.6982486  -14.979211 -14.275662
## mesocosm1     93.0259208   83.91737   0.9539806 -175.464508  -1.099292
## mesocosm2      0.9088432  -78.62335  54.9685962   75.039373  48.816422
##                   [,6]      [,7]        [,8]
## (Intercept)  233.93560 215.95196  175.774567
## treatment1    40.69503  52.02059   85.352178
## treatment2  -104.60326  14.78854   43.297898
## treatment3    37.30763  30.87268 -180.206402
## mesocosm1    -28.43778 -34.88477  -35.883704
## mesocosm2    -39.64341 -31.78154    4.431836
## 
## $f.perms
##              [,1]      [,2]
##    [1,] 0.7432300 0.7290456
##    [2,] 0.8848041 1.1393322
##    [3,] 1.1729035 1.2457257
##    [4,] 1.2843731 1.2531550
##    [5,] 1.1661533 1.2386751
##    [6,] 0.9762207 1.2699893
##    [7,] 1.0383574 1.2328177
##    [8,] 1.0142775 1.1383605
##    [9,] 1.2440237 1.4470576
##   [10,] 1.2909057 1.1648786
##   [11,] 1.0868488 0.8842503
##   [12,] 1.1366950 1.0308384
##   [13,] 0.9472805 0.8951887
##   [14,] 1.1935284 0.9320958
##   [15,] 0.9051350 1.6214595
##   [16,] 0.8705855 1.4068916
##   [17,] 0.8772709 0.7506268
##   [18,] 1.0002977 0.8710072
##   [19,] 1.1587166 1.5219865
##   [20,] 1.1156383 1.1566790
##   [21,] 1.2259409 1.1489937
##   [22,] 0.9715496 0.7464401
##   [23,] 1.1631281 1.0388870
##   [24,] 0.7450840 0.8273387
##   [25,] 1.2484102 1.0943515
##   [26,] 1.1758260 0.8503507
##   [27,] 1.0971235 0.9871757
##   [28,] 1.3676104 1.1847837
##   [29,] 0.9825889 0.7885293
##   [30,] 1.1789334 1.0870052
##   [31,] 1.0679314 0.8120387
##   [32,] 0.9873380 1.0807963
##   [33,] 0.7624097 0.8718371
##   [34,] 1.2316787 1.0346532
##   [35,] 0.9632473 0.9803554
##   [36,] 0.8271822 0.8622812
##   [37,] 1.3027186 1.2669479
##   [38,] 0.8032766 0.7908005
##   [39,] 0.8237494 0.8833290
##   [40,] 0.9242755 0.8554582
##   [41,] 0.7904664 0.8779048
##   [42,] 1.2834472 1.3115194
##   [43,] 1.1772781 1.1070936
##   [44,] 1.1272167 0.8038415
##   [45,] 1.0304728 0.9896683
##   [46,] 1.0591602 1.2287034
##   [47,] 1.0498093 1.0008169
##   [48,] 0.9332430 1.2599122
##   [49,] 0.8032766 0.7908005
##   [50,] 0.8957059 1.0526557
##   [51,] 0.7305756 0.7245254
##   [52,] 0.8660802 0.9463642
##   [53,] 1.5278710 1.3253900
##   [54,] 1.0423602 1.0765188
##   [55,] 0.7081438 0.8523895
##   [56,] 0.7713326 0.8220269
##   [57,] 1.4107214 1.2090405
##   [58,] 1.1053067 1.2695645
##   [59,] 1.2251476 1.2113237
##   [60,] 0.8723904 1.0441015
##   [61,] 1.2206784 1.2832185
##   [62,] 1.0108054 1.5236366
##   [63,] 0.9650073 0.8593427
##   [64,] 1.1643856 1.2840502
##   [65,] 0.7380623 0.7132953
##   [66,] 1.0475921 0.8191648
##   [67,] 1.1238935 1.5055739
##   [68,] 1.5088466 1.2608009
##   [69,] 0.7431952 0.9440782
##   [70,] 1.0435922 1.2091706
##   [71,] 0.8711250 1.3910917
##   [72,] 1.2855191 1.0244809
##   [73,] 0.8982184 0.7970368
##   [74,] 1.1971719 1.0178351
##   [75,] 1.0432567 1.1602877
##   [76,] 1.3054402 1.0781414
##   [77,] 0.7702279 0.9028931
##   [78,] 0.8252817 1.2206159
##   [79,] 0.8017169 0.9839279
##   [80,] 0.8868576 0.9792648
##   [81,] 1.1484534 1.1171440
##   [82,] 1.2024761 1.2257083
##   [83,] 0.8468687 1.2027705
##   [84,] 0.7996538 0.6782247
##   [85,] 0.8845318 1.1090785
##   [86,] 0.8220240 0.9205147
##   [87,] 0.8561634 1.0791637
##   [88,] 0.8308943 0.7535672
##   [89,] 1.0621480 0.8685895
##   [90,] 1.1053458 1.5870095
##   [91,] 0.8793322 0.6761034
##   [92,] 1.0160962 1.0899453
##   [93,] 0.8800792 0.8498708
##   [94,] 1.0090250 1.0894036
##   [95,] 1.0118802 0.8101927
##   [96,] 1.1025787 1.0685203
##   [97,] 1.0682680 1.2064726
##   [98,] 1.3847534 1.0380336
##   [99,] 1.2355100 1.2258142
##  [100,] 0.9849165 0.9670736
##  [101,] 0.8002744 0.9078132
##  [102,] 1.0240818 1.3668708
##  [103,] 0.9163828 0.8301982
##  [104,] 0.8049490 0.6826231
##  [105,] 1.0627728 1.0540134
##  [106,] 0.9590478 0.7436096
##  [107,] 1.2042549 1.2619442
##  [108,] 1.0078700 0.8860917
##  [109,] 1.4491191 1.0473471
##  [110,] 1.3020159 1.1153027
##  [111,] 0.9101772 1.0437645
##  [112,] 1.6002879 1.2167646
##  [113,] 0.5996318 0.7863096
##  [114,] 0.7556862 0.7193096
##  [115,] 0.7380623 0.7132953
##  [116,] 1.1196108 1.2610868
##  [117,] 0.9626846 0.8927032
##  [118,] 0.8753691 1.0215512
##  [119,] 0.8305596 0.9339126
##  [120,] 0.9176752 0.7706018
##  [121,] 0.7279569 0.9229149
##  [122,] 1.1920712 1.1086911
##  [123,] 0.7839264 1.1033396
##  [124,] 1.0266995 0.9842949
##  [125,] 0.8529498 0.7770916
##  [126,] 0.8815990 1.0824962
##  [127,] 0.9137536 0.8224032
##  [128,] 1.1234907 0.8765672
##  [129,] 0.9221499 0.9523065
##  [130,] 0.6428772 0.7645898
##  [131,] 0.7729761 1.1197652
##  [132,] 0.9022983 0.7868282
##  [133,] 1.3427224 1.6489040
##  [134,] 1.1581972 1.0881731
##  [135,] 1.1359992 1.0468282
##  [136,] 1.1849999 1.0410305
##  [137,] 1.2552694 0.8653186
##  [138,] 1.6232882 1.3300176
##  [139,] 0.7467571 0.8240233
##  [140,] 1.0758731 0.9952841
##  [141,] 1.0867904 1.0216499
##  [142,] 0.8222308 0.9948299
##  [143,] 0.9984302 0.8293392
##  [144,] 1.4338513 1.1289216
##  [145,] 1.1352970 1.0235945
##  [146,] 1.3385660 1.4283348
##  [147,] 1.2739661 1.2699985
##  [148,] 1.0487565 1.0036733
##  [149,] 0.9862880 1.0067037
##  [150,] 0.8692255 1.0345805
##  [151,] 1.2453856 1.0232991
##  [152,] 0.9052224 0.7806184
##  [153,] 1.1466167 1.3349460
##  [154,] 0.9626035 0.9700306
##  [155,] 1.1563831 1.1503212
##  [156,] 0.8447534 0.9876010
##  [157,] 0.9665781 0.7374020
##  [158,] 0.7026891 0.7135412
##  [159,] 1.0748182 1.1549036
##  [160,] 1.1154189 1.7216405
##  [161,] 0.8844257 1.0424663
##  [162,] 1.5382710 1.2116392
##  [163,] 0.9554399 0.9993463
##  [164,] 1.6126565 0.9728582
##  [165,] 0.8447534 0.9876010
##  [166,] 0.8034283 0.7738834
##  [167,] 1.1109500 0.9196886
##  [168,] 0.8686215 1.1463551
##  [169,] 0.9828684 1.1330532
##  [170,] 1.3511568 0.9885872
##  [171,] 1.0627781 1.2038449
##  [172,] 0.8454306 1.1201587
##  [173,] 0.8295094 0.8047893
##  [174,] 0.6819924 0.7703660
##  [175,] 0.9503398 1.0960767
##  [176,] 1.1312011 1.1247277
##  [177,] 0.8043872 1.1643945
##  [178,] 0.9906194 0.8476070
##  [179,] 1.0438388 0.9986090
##  [180,] 0.9213236 1.1331417
##  [181,] 1.1946350 0.8700822
##  [182,] 1.3475243 1.6362418
##  [183,] 1.1279432 1.4877999
##  [184,] 0.9568513 1.2209783
##  [185,] 1.3874981 1.1726753
##  [186,] 0.9980952 0.9505409
##  [187,] 1.1758231 1.4332615
##  [188,] 1.0395913 0.8530052
##  [189,] 1.1756222 1.0111008
##  [190,] 0.8382599 0.6971862
##  [191,] 1.0417203 1.4589833
##  [192,] 0.9600333 1.1485001
##  [193,] 0.8150170 1.1156813
##  [194,] 0.9660563 0.7385898
##  [195,] 1.4724445 1.0296665
##  [196,] 1.1520593 1.0732521
##  [197,] 0.9655325 1.1244586
##  [198,] 0.8545088 1.0418258
##  [199,] 0.9902210 0.9370593
##  [200,] 0.9351145 0.8454048
##  [201,] 0.7595082 0.7228276
##  [202,] 1.1115951 0.9663346
##  [203,] 0.8883194 0.9545668
##  [204,] 1.1614752 0.9651173
##  [205,] 0.6837178 0.7585924
##  [206,] 1.2979338 1.7474778
##  [207,] 1.5846176 0.9385564
##  [208,] 1.3686543 1.1680443
##  [209,] 0.8821753 0.8292486
##  [210,] 0.9192173 1.2028365
##  [211,] 1.0952258 1.0340416
##  [212,] 0.9039094 1.4103517
##  [213,] 0.9297073 1.0876186
##  [214,] 0.8329996 0.7271277
##  [215,] 0.9266372 1.1029412
##  [216,] 0.9051679 0.8021581
##  [217,] 0.7839981 0.8110185
##  [218,] 1.1323642 0.8768544
##  [219,] 1.2055691 1.1335921
##  [220,] 1.1972791 1.3511773
##  [221,] 0.7185941 1.1406997
##  [222,] 1.3117203 0.8868842
##  [223,] 0.9594445 0.9018044
##  [224,] 1.2040838 0.9996577
##  [225,] 0.8031306 0.6564312
##  [226,] 0.8382843 0.7404572
##  [227,] 0.8995725 0.8765970
##  [228,] 1.2235578 1.2784028
##  [229,] 0.8941813 1.2585593
##  [230,] 1.3224874 1.1979515
##  [231,] 1.2326986 1.3306668
##  [232,] 1.3220764 0.9017813
##  [233,] 1.1082017 0.9706791
##  [234,] 0.9614362 0.7672277
##  [235,] 1.0995430 0.9947156
##  [236,] 1.0744895 0.9792030
##  [237,] 1.1723636 1.3912338
##  [238,] 0.8538354 0.7722189
##  [239,] 1.0364957 0.7854749
##  [240,] 1.0363267 1.3487848
##  [241,] 0.9331471 0.8457439
##  [242,] 0.9026486 1.1032858
##  [243,] 1.2497056 1.3936438
##  [244,] 1.1979528 1.0063743
##  [245,] 0.9139260 1.0250877
##  [246,] 0.7959954 0.8241530
##  [247,] 0.7735754 0.8365933
##  [248,] 1.1069141 1.0159037
##  [249,] 1.1315307 0.9327468
##  [250,] 1.0857164 0.9707354
##  [251,] 0.8218004 1.0519578
##  [252,] 0.9482724 1.3162431
##  [253,] 1.2485172 1.1595695
##  [254,] 0.9702265 0.7331800
##  [255,] 1.2937664 1.5641234
##  [256,] 0.7203793 0.7127856
##  [257,] 1.2331633 1.3220200
##  [258,] 0.8343440 1.1015939
##  [259,] 1.0562401 0.7426243
##  [260,] 1.6483921 1.1446084
##  [261,] 1.0696576 0.8661267
##  [262,] 0.9998754 1.1339141
##  [263,] 1.1632407 1.2183130
##  [264,] 1.1363847 0.9281885
##  [265,] 0.8350799 1.0631138
##  [266,] 1.4289681 1.1720306
##  [267,] 1.0900875 1.5671717
##  [268,] 0.8700256 0.9777232
##  [269,] 1.2355100 1.2258142
##  [270,] 1.0706318 0.7690173
##  [271,] 0.7201013 0.8692919
##  [272,] 1.0168847 0.8674753
##  [273,] 0.8303148 0.6100093
##  [274,] 1.2511181 1.1408004
##  [275,] 0.9694617 1.0123459
##  [276,] 1.1999062 1.1633724
##  [277,] 1.3171473 1.5466504
##  [278,] 0.7221675 0.7996023
##  [279,] 0.8806655 0.8348362
##  [280,] 0.7987340 0.8568488
##  [281,] 1.2974558 1.2347233
##  [282,] 1.3127256 0.7767086
##  [283,] 1.0311266 1.0599598
##  [284,] 0.8172455 0.8062857
##  [285,] 1.0588791 1.5997168
##  [286,] 1.2958975 0.9475313
##  [287,] 1.3249512 1.1596692
##  [288,] 1.0099780 1.0595175
##  [289,] 0.8568625 0.9421204
##  [290,] 1.1137262 0.8132602
##  [291,] 1.1552954 0.9188925
##  [292,] 1.1153707 1.1315907
##  [293,] 1.3155042 1.2806660
##  [294,] 1.1259612 0.8969692
##  [295,] 0.6937793 0.7526855
##  [296,] 0.7884424 0.9188577
##  [297,] 0.7941456 0.7633577
##  [298,] 0.8371353 0.8552119
##  [299,] 1.2195161 0.8188984
##  [300,] 1.1755937 1.0454839
##  [301,] 0.8798818 0.9740269
##  [302,] 0.8577083 0.8062474
##  [303,] 0.7425737 0.8670075
##  [304,] 1.0335909 0.9749049
##  [305,] 1.0154237 0.8715373
##  [306,] 0.9792095 0.9149300
##  [307,] 1.0300904 0.8070708
##  [308,] 0.8262698 0.7041550
##  [309,] 0.9344937 0.7018929
##  [310,] 0.8558931 0.9218720
##  [311,] 0.6098885 0.6336836
##  [312,] 1.2102545 1.1549818
##  [313,] 1.0374878 1.0039909
##  [314,] 1.3101459 1.3729620
##  [315,] 1.4083880 1.0591829
##  [316,] 0.7864359 1.3042747
##  [317,] 0.9847186 1.0810657
##  [318,] 1.2703952 1.2622606
##  [319,] 0.9026781 1.4549062
##  [320,] 1.0342787 0.8670581
##  [321,] 1.1620854 1.0730936
##  [322,] 1.2885726 1.5537821
##  [323,] 0.8241052 0.8168143
##  [324,] 0.6383500 0.8100499
##  [325,] 0.7201013 0.8692919
##  [326,] 1.0736092 0.9118332
##  [327,] 1.3032672 1.0644664
##  [328,] 1.1412659 0.9740561
##  [329,] 0.6819924 0.7703660
##  [330,] 1.1579474 1.2125217
##  [331,] 1.2123994 1.4487672
##  [332,] 1.1034591 1.0909689
##  [333,] 1.0277445 1.0912345
##  [334,] 0.7675850 0.8223711
##  [335,] 0.8485530 0.9025921
##  [336,] 0.8787281 0.9563947
##  [337,] 0.9547429 0.8631603
##  [338,] 0.8503184 1.0214666
##  [339,] 1.1637619 0.8071561
##  [340,] 0.9414528 0.9837976
##  [341,] 1.0385842 1.1894942
##  [342,] 0.9329144 1.0976958
##  [343,] 1.1482845 0.9841769
##  [344,] 0.9710925 1.1093762
##  [345,] 1.1776952 1.3101582
##  [346,] 0.8617724 1.1294312
##  [347,] 0.9357138 0.8472916
##  [348,] 0.8671483 0.8873105
##  [349,] 1.1082017 0.9706791
##  [350,] 0.7641790 0.9988863
##  [351,] 0.7304439 0.9477912
##  [352,] 0.7981525 1.0867010
##  [353,] 0.8024708 1.0015053
##  [354,] 1.0011521 0.9284265
##  [355,] 1.3430628 1.0893263
##  [356,] 0.8580596 1.0924195
##  [357,] 1.3430628 1.0893263
##  [358,] 0.9704394 0.7717346
##  [359,] 0.9099438 0.8134119
##  [360,] 1.1354301 0.9237335
##  [361,] 1.1811040 1.0081950
##  [362,] 0.9372136 1.3676703
##  [363,] 0.9109529 0.8889209
##  [364,] 0.8576258 0.7022479
##  [365,] 1.1570311 1.1488258
##  [366,] 1.2715888 1.1416619
##  [367,] 0.6654308 0.6110134
##  [368,] 0.9656467 1.2309415
##  [369,] 0.8408822 0.6588805
##  [370,] 0.7508323 0.6960002
##  [371,] 0.9457489 0.9722680
##  [372,] 1.1070964 1.0162357
##  [373,] 1.0204380 0.9048081
##  [374,] 0.8608226 0.7501220
##  [375,] 0.9783103 0.7584903
##  [376,] 1.2787754 1.1867816
##  [377,] 1.0767359 1.0407425
##  [378,] 0.9254334 1.1774310
##  [379,] 1.0166497 0.8726625
##  [380,] 1.1652604 1.0502082
##  [381,] 0.8997930 0.9987615
##  [382,] 0.8191449 0.7831592
##  [383,] 0.7729761 1.1197652
##  [384,] 0.9923486 1.0783053
##  [385,] 1.2768595 0.9987965
##  [386,] 0.8477528 0.8526568
##  [387,] 0.9065177 1.1079101
##  [388,] 1.0190916 1.2802860
##  [389,] 0.7513454 0.8352306
##  [390,] 0.9606308 1.0944313
##  [391,] 1.0249804 1.0675015
##  [392,] 1.0280806 1.0887470
##  [393,] 1.3357941 1.1159913
##  [394,] 1.0117345 1.1000931
##  [395,] 1.0550385 1.0107831
##  [396,] 1.0827496 1.3283138
##  [397,] 0.8961323 0.7662075
##  [398,] 1.2849221 1.4791837
##  [399,] 1.0902238 1.0709330
##  [400,] 0.8645608 0.9776457
##  [401,] 0.8678541 0.8978296
##  [402,] 0.9678981 0.7836034
##  [403,] 1.2230407 1.0208443
##  [404,] 1.1836389 1.1715781
##  [405,] 1.0205978 0.7711841
##  [406,] 0.7205731 0.8806508
##  [407,] 0.9019794 1.0773983
##  [408,] 0.9755404 1.3351241
##  [409,] 0.6458772 0.7708382
##  [410,] 0.9522845 1.1647121
##  [411,] 1.0731929 0.9572806
##  [412,] 0.9017736 1.2471709
##  [413,] 1.0720613 0.9673269
##  [414,] 1.2064433 1.0411995
##  [415,] 0.9629891 0.8792525
##  [416,] 0.9839227 0.9078603
##  [417,] 1.4756935 1.5206051
##  [418,] 0.9413685 0.9341802
##  [419,] 1.0459937 0.8802594
##  [420,] 1.0040117 1.0628899
##  [421,] 1.0459402 0.9281636
##  [422,] 1.0159540 1.1533443
##  [423,] 0.6712048 0.7979289
##  [424,] 1.1912014 1.0914399
##  [425,] 1.2481819 1.0946940
##  [426,] 1.0734577 1.0223033
##  [427,] 0.9181386 0.8680468
##  [428,] 0.8653306 0.9991803
##  [429,] 1.1916916 1.3749025
##  [430,] 0.8804077 0.9394879
##  [431,] 1.0761988 0.9243846
##  [432,] 0.9669020 0.8692008
##  [433,] 0.9906832 1.1561789
##  [434,] 0.8693110 1.0342403
##  [435,] 0.9333090 0.7198639
##  [436,] 1.0035544 0.9707470
##  [437,] 1.0866364 1.0762600
##  [438,] 0.9089298 0.8040779
##  [439,] 0.9025755 1.0581354
##  [440,] 0.9672256 0.8630771
##  [441,] 0.6606696 0.7505885
##  [442,] 0.7343585 0.6626696
##  [443,] 1.2661354 0.9972923
##  [444,] 1.0588791 1.5997168
##  [445,] 1.1422562 0.8957095
##  [446,] 1.0000227 0.8403757
##  [447,] 1.2187644 1.0450269
##  [448,] 0.7160874 0.8087225
##  [449,] 1.2840253 1.4277105
##  [450,] 1.0287436 0.8662763
##  [451,] 0.7609265 0.7741278
##  [452,] 0.8577083 0.8062474
##  [453,] 1.0938305 1.2240903
##  [454,] 1.3231165 1.0066589
##  [455,] 0.9685278 0.8754577
##  [456,] 1.4247096 1.3430121
##  [457,] 1.0603430 1.1301982
##  [458,] 1.0194594 1.1165507
##  [459,] 1.1811043 0.9666363
##  [460,] 1.1224475 1.1188030
##  [461,] 1.0158795 1.4252551
##  [462,] 0.8177702 1.0702889
##  [463,] 1.0446336 1.0228291
##  [464,] 1.4082930 1.1004577
##  [465,] 0.8058251 0.8789579
##  [466,] 0.8575410 1.1369456
##  [467,] 0.8458905 0.6764756
##  [468,] 1.5553640 1.1470304
##  [469,] 0.8480415 0.9319952
##  [470,] 0.9671782 1.1320962
##  [471,] 1.0877613 0.7969110
##  [472,] 1.0970825 0.9820640
##  [473,] 1.1109447 1.1734071
##  [474,] 1.0256023 0.9078277
##  [475,] 1.1224475 1.1188030
##  [476,] 1.0569459 1.0077200
##  [477,] 0.8377330 0.9000889
##  [478,] 0.9429982 1.1610337
##  [479,] 1.0108540 0.9599224
##  [480,] 0.9841025 0.8803462
##  [481,] 0.7950604 0.7541514
##  [482,] 0.8926647 0.9977331
##  [483,] 1.1049894 1.0475700
##  [484,] 1.0427106 1.0665158
##  [485,] 0.8216805 0.7394078
##  [486,] 0.9650073 0.8593427
##  [487,] 0.9307793 0.9420379
##  [488,] 0.9800005 1.1073308
##  [489,] 0.9742092 1.1532089
##  [490,] 1.0531160 0.9590087
##  [491,] 0.9031006 1.2097163
##  [492,] 1.0705071 1.1143376
##  [493,] 0.9315494 0.8876666
##  [494,] 0.9923904 0.9732259
##  [495,] 0.7435298 0.9022891
##  [496,] 0.7498624 1.0731308
##  [497,] 0.8202513 0.7006957
##  [498,] 1.2460853 1.2187822
##  [499,] 1.1656207 1.1483668
##  [500,] 0.7384633 0.7179564
##  [501,] 0.8164700 0.9199817
##  [502,] 0.9211658 1.3160305
##  [503,] 0.8100908 0.7394400
##  [504,] 1.0678303 1.0517109
##  [505,] 1.0087395 0.9114826
##  [506,] 1.0858561 0.7997689
##  [507,] 1.1326220 1.0404578
##  [508,] 0.6878003 0.8423828
##  [509,] 1.0751826 0.8521172
##  [510,] 0.9746611 0.7778360
##  [511,] 0.9722053 1.0688848
##  [512,] 1.1482845 0.9841769
##  [513,] 0.8878019 0.9771136
##  [514,] 1.2909644 1.2539672
##  [515,] 1.3227316 1.1042516
##  [516,] 1.3384108 1.2070471
##  [517,] 1.1648123 1.3540186
##  [518,] 0.9325715 1.0297739
##  [519,] 1.1220462 1.1491292
##  [520,] 0.9734757 1.2150143
##  [521,] 1.3873381 0.9956590
##  [522,] 1.1966519 0.9830317
##  [523,] 0.9748626 0.9282562
##  [524,] 0.8703140 0.7588074
##  [525,] 0.9747261 1.0791088
##  [526,] 1.2928473 0.9211589
##  [527,] 0.9418263 1.2606525
##  [528,] 1.0745730 1.0147207
##  [529,] 0.8988959 1.3460619
##  [530,] 0.8761603 0.9700818
##  [531,] 0.8402399 0.8836138
##  [532,] 1.0900875 1.5671717
##  [533,] 1.1040966 1.1322706
##  [534,] 0.8669744 0.8159013
##  [535,] 1.2690419 1.1596714
##  [536,] 0.8630533 1.3296351
##  [537,] 0.9217755 1.0380671
##  [538,] 1.0734197 1.2011946
##  [539,] 0.8620939 1.0551877
##  [540,] 1.2778042 0.9564672
##  [541,] 1.0182885 1.1403780
##  [542,] 0.7835713 0.8215995
##  [543,] 1.0487127 1.0669901
##  [544,] 1.1663450 0.8948868
##  [545,] 0.7318668 0.8224036
##  [546,] 1.0006834 1.0405641
##  [547,] 1.0046995 0.9157709
##  [548,] 1.1464227 1.5053366
##  [549,] 1.1836389 1.1715781
##  [550,] 1.0868488 0.8842503
##  [551,] 1.0217093 1.0137670
##  [552,] 0.9140999 1.0171548
##  [553,] 1.3256991 1.1885931
##  [554,] 1.3681454 1.2274806
##  [555,] 1.1268771 0.8864934
##  [556,] 0.9767327 0.9458234
##  [557,] 1.1881847 0.8240778
##  [558,] 1.2161012 1.0726461
##  [559,] 1.1315307 0.9327468
##  [560,] 1.3980779 1.6661502
##  [561,] 0.8375667 1.1250763
##  [562,] 1.0244100 0.9526910
##  [563,] 0.9116596 0.9123032
##  [564,] 0.9503398 1.0960767
##  [565,] 0.9878519 1.0508807
##  [566,] 1.2561456 1.1676613
##  [567,] 1.2222982 1.1971813
##  [568,] 1.0376711 1.1130915
##  [569,] 0.8440259 0.7584026
##  [570,] 1.1254600 1.0763480
##  [571,] 0.7898536 0.8362042
##  [572,] 0.8333403 0.6953010
##  [573,] 0.8274119 1.1643755
##  [574,] 0.7651458 0.6990907
##  [575,] 0.9771385 1.1901387
##  [576,] 0.9123806 0.7431241
##  [577,] 1.0549996 0.8637318
##  [578,] 0.9401383 0.9667807
##  [579,] 0.9140212 0.8348729
##  [580,] 1.0740119 0.8860561
##  [581,] 1.4774841 1.1756168
##  [582,] 0.7530957 0.9233284
##  [583,] 1.3148085 1.2149868
##  [584,] 0.7531149 0.7480792
##  [585,] 0.7572124 0.8886228
##  [586,] 1.4945500 1.0630824
##  [587,] 0.8547373 0.7024478
##  [588,] 0.9276885 1.0412067
##  [589,] 0.8136193 0.9866351
##  [590,] 0.8227415 0.7425148
##  [591,] 0.7491985 0.7198720
##  [592,] 1.1417959 0.9577219
##  [593,] 1.1574054 1.2898111
##  [594,] 0.9733418 1.1621398
##  [595,] 0.9359426 0.9363046
##  [596,] 1.2749921 1.1642129
##  [597,] 0.8376985 1.0859326
##  [598,] 1.0653420 0.8379374
##  [599,] 1.0655495 0.8503483
##  [600,] 0.6641117 0.6991013
##  [601,] 0.8149746 0.9409815
##  [602,] 0.9647565 0.8175196
##  [603,] 0.7614665 1.1032848
##  [604,] 0.6811288 0.7987900
##  [605,] 0.7489591 0.6553583
##  [606,] 0.9384281 0.9978513
##  [607,] 1.1606516 0.9237399
##  [608,] 1.2261988 1.1088926
##  [609,] 1.2523450 1.0109429
##  [610,] 1.3382519 1.6501505
##  [611,] 1.1863434 1.2484821
##  [612,] 1.1137262 0.8132602
##  [613,] 1.0524666 0.8042643
##  [614,] 0.9200786 0.7207448
##  [615,] 0.7429249 0.8742993
##  [616,] 1.0590001 0.8707991
##  [617,] 1.0408307 1.0797895
##  [618,] 0.8365314 1.0295250
##  [619,] 1.0338608 1.3522022
##  [620,] 1.2675149 1.7582639
##  [621,] 0.7384719 0.7543820
##  [622,] 1.1344139 1.0717874
##  [623,] 0.9179360 0.9762059
##  [624,] 0.9025292 0.8379018
##  [625,] 0.8892221 1.0561745
##  [626,] 0.8260558 0.8722955
##  [627,] 1.1919093 0.9364194
##  [628,] 0.8456699 0.8925614
##  [629,] 0.8456699 0.8925614
##  [630,] 0.8630533 1.3296351
##  [631,] 1.0992838 1.3056771
##  [632,] 0.7840449 0.7876083
##  [633,] 0.7978897 0.7436696
##  [634,] 0.9140999 1.0171548
##  [635,] 0.9365634 0.9335736
##  [636,] 0.8464276 0.6614314
##  [637,] 1.1892676 1.1203350
##  [638,] 1.0612980 1.1152750
##  [639,] 1.1013617 0.9647660
##  [640,] 1.1226515 1.0195536
##  [641,] 0.7986581 0.7975907
##  [642,] 1.1555691 1.0282626
##  [643,] 1.1439279 1.1729466
##  [644,] 0.9724622 0.9597829
##  [645,] 1.0699198 1.0196484
##  [646,] 1.6483921 1.1446084
##  [647,] 0.7716332 0.7202555
##  [648,] 1.0885960 1.0582116
##  [649,] 1.1210231 1.1922589
##  [650,] 1.1688021 1.3164127
##  [651,] 1.3426549 1.2254040
##  [652,] 1.1983633 1.1582813
##  [653,] 0.7756160 0.8120904
##  [654,] 1.2840253 1.4277105
##  [655,] 0.9954563 1.0738855
##  [656,] 0.9695817 1.0784741
##  [657,] 0.8891768 0.8525692
##  [658,] 1.1947082 1.5932502
##  [659,] 1.2236204 1.0613673
##  [660,] 0.8489543 0.7770526
##  [661,] 0.9894320 0.8771653
##  [662,] 0.8599440 0.9360397
##  [663,] 1.0447079 1.1098183
##  [664,] 1.3369120 1.2187638
##  [665,] 1.2835229 1.1956830
##  [666,] 0.9025292 0.8379018
##  [667,] 0.9575264 0.8725267
##  [668,] 1.0401908 1.2137431
##  [669,] 1.1897222 1.2413359
##  [670,] 1.2705004 1.3309396
##  [671,] 1.1429473 1.3924462
##  [672,] 1.1949040 0.8680605
##  [673,] 0.8503457 0.8844881
##  [674,] 1.2184506 1.2252641
##  [675,] 1.0104799 0.9991337
##  [676,] 1.0359170 0.9270151
##  [677,] 0.9437935 0.8469207
##  [678,] 0.7456392 0.9714163
##  [679,] 0.8458905 0.6764756
##  [680,] 0.9830624 1.0295725
##  [681,] 0.9326259 0.9495122
##  [682,] 0.8370918 0.9216414
##  [683,] 0.9038528 1.0769603
##  [684,] 0.9450050 0.6586791
##  [685,] 0.9342991 0.9397451
##  [686,] 0.9839197 0.9247637
##  [687,] 0.9633922 1.1683682
##  [688,] 1.2594626 1.0777730
##  [689,] 0.9060723 0.9973914
##  [690,] 0.8630356 0.7010245
##  [691,] 1.0905454 0.8019209
##  [692,] 1.3500625 1.1831205
##  [693,] 0.9275484 0.9602443
##  [694,] 1.1082017 0.9706791
##  [695,] 1.0197455 0.8823804
##  [696,] 0.7389218 0.9588984
##  [697,] 0.9712378 0.8953497
##  [698,] 0.9647791 0.9653939
##  [699,] 1.0599807 1.2112246
##  [700,] 1.3676255 1.2578093
##  [701,] 1.0140709 1.2175774
##  [702,] 1.0071987 0.9540930
##  [703,] 0.7637382 0.6837297
##  [704,] 1.1925366 1.0606423
##  [705,] 1.1168212 0.7459893
##  [706,] 0.9716694 0.7465534
##  [707,] 1.0463054 0.8374901
##  [708,] 1.2799074 1.1725563
##  [709,] 1.0257467 0.8481767
##  [710,] 0.9901180 0.9197178
##  [711,] 0.9223333 1.3916730
##  [712,] 0.9588444 0.8362203
##  [713,] 1.2236864 1.0115105
##  [714,] 0.9862880 1.0067037
##  [715,] 1.2355342 0.9832187
##  [716,] 1.0542445 1.3450124
##  [717,] 0.9191070 0.9340009
##  [718,] 0.8246598 0.7951642
##  [719,] 1.1613623 1.2454465
##  [720,] 1.0786600 0.9305868
##  [721,] 1.0078559 0.9467545
##  [722,] 0.6973789 0.5630912
##  [723,] 0.7576445 0.8996450
##  [724,] 0.6383500 0.8100499
##  [725,] 1.1729035 1.2457257
##  [726,] 0.9026883 0.9640071
##  [727,] 0.9656666 0.7172135
##  [728,] 1.1456106 1.1117206
##  [729,] 1.2889299 1.3348074
##  [730,] 1.0337772 0.9605482
##  [731,] 1.1369760 1.3041239
##  [732,] 1.2499462 1.3430898
##  [733,] 1.0480965 0.8896668
##  [734,] 1.0616841 1.1896894
##  [735,] 1.2501589 1.1953851
##  [736,] 0.6743576 0.7382286
##  [737,] 1.0450767 1.0164158
##  [738,] 0.7847498 1.0414661
##  [739,] 1.1852873 1.0481159
##  [740,] 0.7940223 0.8699340
##  [741,] 0.9998463 0.9464617
##  [742,] 0.9377773 1.1488593
##  [743,] 0.8195256 1.1715121
##  [744,] 0.7958550 1.0102027
##  [745,] 0.8547373 0.7024478
##  [746,] 0.7013885 0.8169594
##  [747,] 0.8386591 0.6406019
##  [748,] 1.0831210 1.3477893
##  [749,] 1.1599025 1.4624492
##  [750,] 0.7410526 0.8883296
##  [751,] 0.6226599 0.7855845
##  [752,] 1.2605829 1.1397543
##  [753,] 0.9418263 1.2606525
##  [754,] 1.1570685 0.9345075
##  [755,] 0.7235885 0.9249239
##  [756,] 0.8442062 1.2380096
##  [757,] 0.9747304 1.0325711
##  [758,] 0.7176625 0.7917586
##  [759,] 1.1423713 0.9246163
##  [760,] 0.8753662 0.9854460
##  [761,] 0.8577762 0.8029649
##  [762,] 0.8982930 1.0751731
##  [763,] 0.9899512 0.8230409
##  [764,] 0.7763803 0.7338416
##  [765,] 0.9029047 1.1672295
##  [766,] 0.9835032 0.9404684
##  [767,] 0.9885856 0.9714552
##  [768,] 0.9471229 1.2169136
##  [769,] 1.2785701 0.9524152
##  [770,] 0.9141406 1.1843143
##  [771,] 1.0331047 1.0780306
##  [772,] 1.0091050 1.1564527
##  [773,] 0.9653938 0.7612914
##  [774,] 1.1983633 1.1582813
##  [775,] 1.0184897 1.1219881
##  [776,] 1.1849674 0.9792517
##  [777,] 1.0724452 1.0876125
##  [778,] 0.7607048 1.0458607
##  [779,] 0.8118340 0.7753697
##  [780,] 0.7926029 0.8842592
##  [781,] 1.2584466 0.9148043
##  [782,] 0.8683132 0.9588256
##  [783,] 0.7723636 0.6358177
##  [784,] 0.9573253 0.8708658
##  [785,] 0.8090954 0.9098716
##  [786,] 0.8551142 0.7327804
##  [787,] 0.7730531 0.6279720
##  [788,] 1.0202442 1.2785571
##  [789,] 1.0327221 0.9179604
##  [790,] 0.8747842 0.9425548
##  [791,] 0.7380623 0.7132953
##  [792,] 1.1295610 0.8421962
##  [793,] 1.2168379 1.0689523
##  [794,] 1.0352350 0.9623957
##  [795,] 1.2600464 1.1982794
##  [796,] 0.7624101 0.8434006
##  [797,] 1.0255241 1.1057440
##  [798,] 0.9823391 0.8974667
##  [799,] 1.5375121 1.2828328
##  [800,] 1.3393128 1.0965608
##  [801,] 1.2663164 0.9499725
##  [802,] 0.8266393 1.0055255
##  [803,] 0.8802133 0.6327773
##  [804,] 1.3690518 0.9622991
##  [805,] 1.0480965 0.8896668
##  [806,] 0.9219862 0.7789198
##  [807,] 1.1109447 1.1734071
##  [808,] 1.2670736 1.2049195
##  [809,] 1.3299877 1.1620649
##  [810,] 1.1174778 1.2431366
##  [811,] 0.9355962 0.9871558
##  [812,] 0.9541660 0.9089694
##  [813,] 1.3208608 1.4059856
##  [814,] 1.1086845 1.2136228
##  [815,] 0.8941419 0.7473888
##  [816,] 0.9069892 0.7656913
##  [817,] 1.5836036 1.1936529
##  [818,] 1.2258614 1.1827716
##  [819,] 0.8740992 0.8421476
##  [820,] 1.0286830 0.9508871
##  [821,] 0.7901561 0.8429813
##  [822,] 1.0343448 0.9716282
##  [823,] 0.9541204 1.2330736
##  [824,] 1.1785130 0.8659577
##  [825,] 1.0051538 1.1639755
##  [826,] 0.8173289 0.9792182
##  [827,] 0.9821411 1.1872049
##  [828,] 1.0618377 0.9223901
##  [829,] 1.1779920 1.2033756
##  [830,] 0.9803460 0.9867396
##  [831,] 1.0006820 0.6866511
##  [832,] 1.3224874 1.1979515
##  [833,] 1.2159931 1.1392421
##  [834,] 0.7646375 0.8167194
##  [835,] 1.2265721 1.2525592
##  [836,] 0.8971072 0.8668499
##  [837,] 0.7428042 0.9452459
##  [838,] 0.9147588 0.9500288
##  [839,] 0.9381240 0.8831160
##  [840,] 0.8995725 0.8765970
##  [841,] 1.1680849 1.0800712
##  [842,] 1.1022839 1.3755116
##  [843,] 0.9301778 1.1993184
##  [844,] 1.1696578 0.9483810
##  [845,] 1.4115457 1.1919290
##  [846,] 1.2843731 1.2531550
##  [847,] 0.8392968 0.9588794
##  [848,] 0.7646617 0.7193727
##  [849,] 0.6989968 0.8639347
##  [850,] 1.1184452 1.1422320
##  [851,] 0.9766480 0.7183557
##  [852,] 1.4380954 1.1810378
##  [853,] 1.2747228 1.3674014
##  [854,] 0.7360438 1.1107325
##  [855,] 0.9818310 0.8848299
##  [856,] 1.1824185 1.2727955
##  [857,] 0.8222603 0.8512002
##  [858,] 1.0414436 1.0754325
##  [859,] 1.0896582 0.9633317
##  [860,] 1.0163045 1.1786328
##  [861,] 1.1059339 1.0031338
##  [862,] 1.4457105 1.2167468
##  [863,] 1.2238554 0.8455165
##  [864,] 0.9015349 0.8340322
##  [865,] 1.2101928 1.0595212
##  [866,] 0.7947965 1.0051645
##  [867,] 0.9711559 1.0583933
##  [868,] 1.2742943 0.9274953
##  [869,] 0.8932214 0.8101462
##  [870,] 0.8254213 0.7749334
##  [871,] 0.9980389 0.8934945
##  [872,] 1.1541856 1.1387317
##  [873,] 1.0827496 1.3283138
##  [874,] 1.1190131 1.1204425
##  [875,] 1.3474126 1.0480663
##  [876,] 1.0793391 0.9705532
##  [877,] 0.9202464 1.0248320
##  [878,] 1.0865528 1.1917000
##  [879,] 1.2780054 1.1404493
##  [880,] 1.2483133 1.4718395
##  [881,] 1.4724445 1.0296665
##  [882,] 1.2211450 1.2689986
##  [883,] 1.1921335 1.5629528
##  [884,] 0.8258451 1.0859529
##  [885,] 0.8795712 0.7206073
##  [886,] 1.1044053 1.1050084
##  [887,] 0.9678294 0.8219174
##  [888,] 1.4133306 1.2855542
##  [889,] 1.2097430 1.0742239
##  [890,] 0.8486594 0.8394173
##  [891,] 0.9594908 0.9701556
##  [892,] 1.0583865 1.1174743
##  [893,] 0.8491222 0.7898913
##  [894,] 1.2513737 0.9627636
##  [895,] 1.2600464 1.1982794
##  [896,] 0.9128490 1.0015652
##  [897,] 0.7376282 0.7072383
##  [898,] 0.8025521 0.9528601
##  [899,] 1.0603430 1.1301982
##  [900,] 1.2470593 0.9093931
##  [901,] 1.1016222 1.2113094
##  [902,] 0.9332430 1.2599122
##  [903,] 1.0243274 0.9201555
##  [904,] 1.3470673 1.3009010
##  [905,] 0.9193640 1.3476005
##  [906,] 0.9249031 0.6875378
##  [907,] 0.6870072 1.0129464
##  [908,] 0.9403708 1.2961274
##  [909,] 1.3819979 1.4264289
##  [910,] 1.0769142 0.8394575
##  [911,] 1.0892338 1.5086742
##  [912,] 0.9882986 1.3066427
##  [913,] 1.2082905 1.1180536
##  [914,] 0.8907446 1.0468391
##  [915,] 0.9432034 0.8943794
##  [916,] 1.1108441 0.9195055
##  [917,] 0.8090954 0.9098716
##  [918,] 0.9980398 1.2117994
##  [919,] 0.9139084 0.9695471
##  [920,] 1.3053631 1.2149067
##  [921,] 0.7625342 0.8029408
##  [922,] 0.8396087 0.8604144
##  [923,] 1.0420899 0.7307147
##  [924,] 1.1021842 1.5490267
##  [925,] 1.0378237 1.2248240
##  [926,] 0.8283966 0.9752297
##  [927,] 1.0553328 1.0168520
##  [928,] 0.8521390 0.7252865
##  [929,] 1.0071513 1.1751846
##  [930,] 1.0706318 0.7690173
##  [931,] 0.9402211 0.7314605
##  [932,] 1.0904697 1.2214437
##  [933,] 0.8241052 0.8168143
##  [934,] 0.7489591 0.6553583
##  [935,] 1.1723204 0.9326249
##  [936,] 1.0214597 0.8979651
##  [937,] 0.8540057 0.8725422
##  [938,] 1.1809822 1.3292428
##  [939,] 1.3346412 1.2580995
##  [940,] 0.8503184 1.0214666
##  [941,] 0.9532520 0.9443186
##  [942,] 1.1372795 0.8092274
##  [943,] 0.8540057 0.8725422
##  [944,] 1.1209882 1.3479401
##  [945,] 0.8704251 0.9494264
##  [946,] 0.9929162 1.0708482
##  [947,] 0.9703802 1.0891121
##  [948,] 1.3564547 1.6125858
##  [949,] 1.1930031 1.4285045
##  [950,] 1.1323496 0.9164400
##  [951,] 1.0244100 0.9526910
##  [952,] 0.9862022 0.7929781
##  [953,] 0.7989311 0.7140332
##  [954,] 0.8665568 1.1252541
##  [955,] 1.2893626 1.2430979
##  [956,] 0.7716332 0.7202555
##  [957,] 1.0307749 0.8801906
##  [958,] 0.8345243 0.8530165
##  [959,] 1.1931884 1.1447288
##  [960,] 0.6705743 0.7560912
##  [961,] 0.8556224 1.1890731
##  [962,] 0.7352058 0.7208831
##  [963,] 1.2928420 1.3402226
##  [964,] 0.7667515 1.1046039
##  [965,] 0.9350848 0.7498842
##  [966,] 1.1190131 1.1204425
##  [967,] 1.1734070 0.9793174
##  [968,] 1.0973462 1.3997457
##  [969,] 1.1621292 1.2062480
##  [970,] 0.9090665 0.6780744
##  [971,] 1.1439279 1.1729466
##  [972,] 1.2094013 0.9341207
##  [973,] 1.5069546 0.9605939
##  [974,] 0.8092907 0.9058913
##  [975,] 1.0006820 0.6866511
##  [976,] 1.3000739 1.1380421
##  [977,] 1.4996559 1.0554235
##  [978,] 0.7494352 0.5976019
##  [979,] 0.6918273 0.8177407
##  [980,] 0.7646617 0.7193727
##  [981,] 1.1043441 0.8327898
##  [982,] 0.8937388 0.9144560
##  [983,] 1.2279257 1.2347776
##  [984,] 0.7885737 0.8453549
##  [985,] 0.8135027 0.8921524
##  [986,] 0.9257986 0.9908109
##  [987,] 1.0042614 0.8501079
##  [988,] 1.1751164 0.9103017
##  [989,] 1.1032416 1.0353574
##  [990,] 1.3899644 1.0960010
##  [991,] 0.9310316 0.9019479
##  [992,] 1.3536862 0.8659661
##  [993,] 1.4865358 0.9605712
##  [994,] 1.1189163 0.8178323
##  [995,] 1.1954913 1.6015319
##  [996,] 1.0902238 1.0709330
##  [997,] 0.9541149 0.7068541
##  [998,] 0.8593658 1.0534316
##  [999,] 1.2783481 0.9189979
## 
## $model.matrix
##   (Intercept) treatment1 treatment2 treatment3 mesocosm1 mesocosm2
## 1           1          1          0          0        -1        -1
## 2           1          1          0          0         0         1
## 3           1         -1         -1         -1        -1        -1
## 4           1          1          0          0         1         0
## 5           1          0          1          0        -1        -1
## 6           1          0          1          0         0         1
## 7           1         -1         -1         -1         0         1
## 8           1          0          0          1         0         1
## 
## $terms
## dist_tab_assay ~ treatment + mesocosm
## attr(,"variables")
## list(dist_tab_assay, treatment, mesocosm)
## attr(,"factors")
##                treatment mesocosm
## dist_tab_assay         0        0
## treatment              1        0
## mesocosm               0        1
## attr(,"term.labels")
## [1] "treatment" "mesocosm" 
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## 
## attr(,"class")
## [1] "adonis"
anova(betadisper(dist_tab_assay,samplesSaccharina$treatment))
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq Mean Sq F value    Pr(>F)    
## Groups     3 18627.4  6209.1  188.23 9.254e-05 ***
## Residuals  4   131.9    33.0                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(betadisper(dist_tab_assay,samplesSaccharina$mesocosm))
## Analysis of Variance Table
## 
## Response: Distances
##           Df  Sum Sq Mean Sq F value  Pr(>F)  
## Groups     2 20684.5 10342.3  8.8147 0.02295 *
## Residuals  5  5866.5  1173.3                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Hedophylum

H_C_vs_T1 <- results(ddsHedophylum,contrast=c("treatment", "C", "T1"))
H_C_vs_T2 <- results(ddsHedophylum,contrast=c("treatment", "C", "T2"))
H_C_vs_T3 <- results(ddsHedophylum,contrast=c("treatment", "C", "T3"))
H_T1_vs_T2 <- results(ddsHedophylum,contrast=c("treatment", "T1", "T2"))
H_T1_vs_T3 <- results(ddsHedophylum,contrast=c("treatment", "T1", "T3"))
H_T2_vs_T3 <- results(ddsHedophylum,contrast=c("treatment", "T2", "T3"))

DESeq2::plotMA(H_C_vs_T1,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T1")

DESeq2::plotMA(H_C_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T2")

DESeq2::plotMA(H_C_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nC vs T3")

DESeq2::plotMA(H_T1_vs_T2,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T2")

DESeq2::plotMA(H_T1_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT1 vs T3")

DESeq2::plotMA(H_T2_vs_T3,ylim=c(-50,50),main="MA-plot for the shrunken log2 fold changes\nT2 vs T3")

vsdHedophylum <- vst(ddsHedophylum, blind=T)

meanSdPlot(assay(vsdHedophylum))

ntd <- normTransform(ddsHedophylum)
meanSdPlot(assay(ntd))

select <- order(rowMeans(counts(ddsHedophylum,normalized=TRUE)),
                decreasing=TRUE)[1:20]
df <- as.data.frame(colData(ddsHedophylum)[,c("treatment","mesocosm")])
pheatmap(assay(vsdHedophylum)[select,], cluster_rows=FALSE, show_rownames=F,
         cluster_cols=FALSE, annotation_col=df)

pcaData <- plotPCA(vsdHedophylum, intgroup=c("treatment", "mesocosm"), returnData=TRUE)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(PC1, PC2, color=treatment, shape=mesocosm)) +
  geom_point(size=3) +
  xlab(paste0("PC1: ",percentVar[1],"% variance")) +
  ylab(paste0("PC2: ",percentVar[2],"% variance")) + 
  coord_fixed()

sampleDists <- dist(t(assay(vsdHedophylum)))
library("RColorBrewer")
sampleDistMatrix <- as.matrix(sampleDists)
rownames(sampleDistMatrix) <- paste(vsdHedophylum$treatment, vsdHedophylum$mesocosm, sep="-")
colnames(sampleDistMatrix) <- NULL
colors <- colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
pheatmap(sampleDistMatrix,
         clustering_distance_rows=sampleDists,
         clustering_distance_cols=sampleDists,
         col=colors)

count_tab_assay <- assay(vsdHedophylum)
dist_tab_assay <- dist(t(count_tab_assay),method="euclidian")
adonis(data=samplesHedophylum,dist_tab_assay ~ treatment + mesocosm, method="euclidian")
## 'adonis' will be deprecated: use 'adonis2' instead
## $aov.tab
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)  
## treatment  3     85025   28342  1.3970 0.46892  0.037 *
## mesocosm   2     35431   17716  0.8732 0.19541  0.738  
## Residuals  3     60864   20288         0.33567         
## Total      8    181320                 1.00000         
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $call
## adonis(formula = dist_tab_assay ~ treatment + mesocosm, data = samplesHedophylum, 
##     method = "euclidian")
## 
## $coefficients
## NULL
## 
## $coef.sites
##                   [,1]       [,2]       [,3]       [,4]       [,5]      [,6]
## (Intercept) 191.970302  206.91199 195.443517 167.888032  182.76783 200.17806
## treatment1    9.572277 -126.89949  71.802839   2.449421   60.40426  31.59877
## treatment2   18.252854   36.45322 -22.585880 -58.462241 -100.95993  14.31881
## treatment3   32.415564   65.13722  -3.577885  52.932541   10.26161  18.56881
## mesocosm1    51.196095   60.61905 -79.843866  41.326635  -13.90914  47.31424
## mesocosm2   -32.394679  -36.34698  40.029982 -70.070501   44.16262 -21.16900
##                    [,7]      [,8]       [,9]
## (Intercept)  200.762289 170.24365 183.824138
## treatment1    70.429605  23.74341 -81.806451
## treatment2     1.102666  37.37274  11.353813
## treatment3  -119.739339 -59.07100  26.012012
## mesocosm1    -20.809615  39.48978 -36.459693
## mesocosm2     55.088379 -64.34184   7.663401
## 
## $f.perms
##              [,1]      [,2]
##    [1,] 1.1321430 1.1478069
##    [2,] 1.1768598 1.2069534
##    [3,] 0.7679314 0.8154228
##    [4,] 1.0426125 0.8992346
##    [5,] 0.9389493 0.9498231
##    [6,] 0.9298261 0.9035579
##    [7,] 0.8098218 1.0363176
##    [8,] 0.9830413 1.1401417
##    [9,] 1.0302974 1.1169251
##   [10,] 1.1228002 1.4044043
##   [11,] 1.1133002 1.2352425
##   [12,] 1.3295294 1.2563451
##   [13,] 0.7888683 0.9158639
##   [14,] 0.8975459 1.0358257
##   [15,] 1.2805290 1.4992478
##   [16,] 1.7139342 1.1804166
##   [17,] 0.7883888 0.9442758
##   [18,] 0.9357620 0.9991741
##   [19,] 1.0470044 1.0456297
##   [20,] 0.8523837 0.7219771
##   [21,] 0.9605456 1.0352997
##   [22,] 0.8674407 1.0406762
##   [23,] 1.0166021 1.2175166
##   [24,] 1.0798034 1.1634646
##   [25,] 1.0591468 0.8023110
##   [26,] 1.0210727 0.9248374
##   [27,] 0.9826087 1.0566507
##   [28,] 1.0456339 1.3242426
##   [29,] 1.1238083 1.4073074
##   [30,] 0.8967747 0.8644003
##   [31,] 1.1556371 1.1444146
##   [32,] 0.8196177 0.9799965
##   [33,] 0.8654207 0.9305841
##   [34,] 0.8760509 0.7124325
##   [35,] 0.9170494 0.8659192
##   [36,] 1.2348309 1.3598966
##   [37,] 1.1551596 1.0297459
##   [38,] 0.9643762 1.1297041
##   [39,] 0.8018289 0.8406504
##   [40,] 1.0583864 0.9545308
##   [41,] 0.8880879 0.8957572
##   [42,] 0.9536734 1.4791547
##   [43,] 1.1980362 1.0264376
##   [44,] 1.3376535 1.0082282
##   [45,] 0.9291911 0.7990284
##   [46,] 1.0083973 0.8697106
##   [47,] 0.8090737 0.9475025
##   [48,] 0.8790558 0.9862338
##   [49,] 0.7622075 0.8820869
##   [50,] 1.1830938 1.1233251
##   [51,] 0.7535054 0.8537869
##   [52,] 1.0305653 0.8911411
##   [53,] 0.8663311 1.2074987
##   [54,] 1.1715601 1.0125958
##   [55,] 0.9413860 1.0715632
##   [56,] 1.0928102 1.1447335
##   [57,] 0.8954735 1.3822699
##   [58,] 0.8840963 1.0617144
##   [59,] 0.8529456 0.8632499
##   [60,] 0.9954049 0.7731098
##   [61,] 1.2109346 1.2909709
##   [62,] 0.7586511 0.7656413
##   [63,] 1.2423205 0.8891954
##   [64,] 0.9608148 0.9899144
##   [65,] 0.7160354 0.7836810
##   [66,] 0.8329551 0.8962249
##   [67,] 1.1839347 0.9543077
##   [68,] 0.9947941 1.2313402
##   [69,] 1.2553788 1.3212578
##   [70,] 0.9604279 0.9162303
##   [71,] 0.7730499 0.8497553
##   [72,] 0.9074982 1.3411797
##   [73,] 1.3709411 1.3638178
##   [74,] 0.9056427 0.8588335
##   [75,] 1.0894069 1.3273672
##   [76,] 0.9257093 1.0439677
##   [77,] 1.0631529 1.3741720
##   [78,] 1.3042598 1.2487848
##   [79,] 0.9505359 1.0790541
##   [80,] 0.8334724 1.1094210
##   [81,] 1.3687242 1.0224612
##   [82,] 1.2242029 1.3639801
##   [83,] 0.8008879 0.6331905
##   [84,] 0.8632762 0.8012244
##   [85,] 0.9837822 0.8267875
##   [86,] 0.8919451 1.2165967
##   [87,] 0.9148583 0.9347649
##   [88,] 0.9516479 0.9462418
##   [89,] 1.0456848 1.0194297
##   [90,] 1.0686699 1.1609035
##   [91,] 0.7938900 0.8118043
##   [92,] 0.9761500 0.8805557
##   [93,] 1.1492592 1.1365248
##   [94,] 1.0993219 1.0020559
##   [95,] 0.9408588 0.8396738
##   [96,] 1.2235383 1.0549834
##   [97,] 1.3766895 1.4219283
##   [98,] 1.4046392 1.1540395
##   [99,] 0.8788570 1.0153963
##  [100,] 0.9327845 0.9363734
##  [101,] 1.2493635 1.5341089
##  [102,] 1.3129810 1.1764534
##  [103,] 1.0003739 0.7887688
##  [104,] 0.8502987 0.6374124
##  [105,] 0.7092845 0.8837503
##  [106,] 1.0083275 0.7159487
##  [107,] 1.1639144 1.0154079
##  [108,] 0.7729203 1.1130778
##  [109,] 1.0416923 0.9873126
##  [110,] 1.1481458 0.8574244
##  [111,] 0.9731228 1.3841536
##  [112,] 0.8645598 0.8510824
##  [113,] 0.8739803 0.8283760
##  [114,] 1.0756493 0.9603158
##  [115,] 0.8933945 0.7964993
##  [116,] 0.8702253 0.9425040
##  [117,] 0.9715425 1.0961130
##  [118,] 0.8997336 0.8297867
##  [119,] 0.8486854 0.8664839
##  [120,] 0.9532945 0.8760506
##  [121,] 0.9153811 0.9106972
##  [122,] 0.8498745 0.8002030
##  [123,] 1.1720971 1.4608453
##  [124,] 0.9524544 0.8842743
##  [125,] 1.2346152 0.9489737
##  [126,] 0.9451158 1.0164001
##  [127,] 1.1647111 1.0236726
##  [128,] 0.9357620 0.9991741
##  [129,] 0.7814634 0.7484747
##  [130,] 1.1248061 1.2094772
##  [131,] 1.0156953 0.8435066
##  [132,] 1.0067029 1.1080189
##  [133,] 1.1580440 1.3742864
##  [134,] 1.1283534 1.4050284
##  [135,] 1.0067065 1.1226071
##  [136,] 0.8564675 1.0454951
##  [137,] 0.8639700 1.1023001
##  [138,] 1.0989870 0.9343298
##  [139,] 1.1586961 0.8567860
##  [140,] 1.1257810 1.1692717
##  [141,] 0.9722789 1.2741987
##  [142,] 0.9709301 0.9823045
##  [143,] 0.9156182 1.0715666
##  [144,] 0.8950958 0.9710177
##  [145,] 1.1857579 0.8549827
##  [146,] 0.7429287 0.9828542
##  [147,] 0.9181432 0.9780481
##  [148,] 1.2148197 1.4069299
##  [149,] 1.6089770 1.4425610
##  [150,] 0.9858647 1.1237383
##  [151,] 0.8676710 0.9883919
##  [152,] 0.9584422 1.2953791
##  [153,] 1.1578946 1.0777763
##  [154,] 0.6836829 0.7116429
##  [155,] 0.9362382 1.0003138
##  [156,] 0.9846352 1.0800894
##  [157,] 0.9769793 0.9673234
##  [158,] 1.0265959 1.5405520
##  [159,] 0.8135127 0.8572326
##  [160,] 0.9637425 0.8817547
##  [161,] 1.1285507 0.9390238
##  [162,] 0.8823821 0.8535617
##  [163,] 0.9160060 1.0173016
##  [164,] 1.3073173 1.0680859
##  [165,] 0.6815780 0.7435194
##  [166,] 0.8292531 0.8770520
##  [167,] 1.0186360 1.4320146
##  [168,] 1.2787233 1.0028932
##  [169,] 1.0428518 1.1960491
##  [170,] 1.0505185 1.0832669
##  [171,] 0.6827824 0.7356287
##  [172,] 1.2571688 1.2518794
##  [173,] 1.0532460 0.9294864
##  [174,] 1.0175737 1.1150419
##  [175,] 1.1804554 1.0528014
##  [176,] 1.1151583 1.1392423
##  [177,] 1.0871661 1.0368060
##  [178,] 1.0516271 0.8354947
##  [179,] 0.8703188 0.7920236
##  [180,] 1.0292662 1.0765568
##  [181,] 1.0623514 1.0581511
##  [182,] 1.0470351 1.2184314
##  [183,] 0.8509324 0.9710055
##  [184,] 1.0013540 1.1024390
##  [185,] 1.0039659 1.0257625
##  [186,] 0.9467356 1.2432149
##  [187,] 1.0226498 0.8230784
##  [188,] 0.9716739 0.8381398
##  [189,] 1.4567044 1.6623065
##  [190,] 1.0409332 0.8243988
##  [191,] 0.8187516 0.9193714
##  [192,] 0.9599542 0.9670411
##  [193,] 0.7694235 0.8964334
##  [194,] 1.0907160 1.3108160
##  [195,] 1.0771941 0.7677632
##  [196,] 1.0311620 0.9595008
##  [197,] 1.0030246 0.9089198
##  [198,] 0.9915413 0.8847789
##  [199,] 0.8561704 0.9390079
##  [200,] 1.1937645 1.2777902
##  [201,] 0.8311430 0.6949905
##  [202,] 1.0883230 1.2987503
##  [203,] 1.2041436 1.3551213
##  [204,] 0.9096595 0.8762071
##  [205,] 0.9424596 0.7227529
##  [206,] 0.9612526 0.9555168
##  [207,] 1.1544519 0.7456926
##  [208,] 0.9993065 0.7968524
##  [209,] 0.7562301 0.8634059
##  [210,] 0.9648673 1.1307306
##  [211,] 0.7679499 0.6241095
##  [212,] 1.2227571 1.2538627
##  [213,] 1.1233643 1.5660015
##  [214,] 0.9596831 0.8881022
##  [215,] 1.1632753 1.2408017
##  [216,] 1.1067871 0.7180954
##  [217,] 0.8943187 0.7936945
##  [218,] 1.1549726 1.0072878
##  [219,] 1.1016265 1.0528801
##  [220,] 1.1464685 0.8960963
##  [221,] 0.9051245 0.8399874
##  [222,] 0.8615505 0.8498742
##  [223,] 0.8068474 0.8940458
##  [224,] 0.9500371 1.1168517
##  [225,] 0.9747147 0.9146335
##  [226,] 1.2036857 0.9725780
##  [227,] 0.8785497 1.2505481
##  [228,] 0.9549476 1.2947114
##  [229,] 1.1045035 1.3403603
##  [230,] 0.8600674 1.0383599
##  [231,] 1.2079979 1.2097727
##  [232,] 0.9991739 1.1511395
##  [233,] 1.0023838 0.7231558
##  [234,] 0.8849399 1.1222081
##  [235,] 1.1471401 1.2365287
##  [236,] 0.9209687 0.9591670
##  [237,] 0.8173010 0.7939589
##  [238,] 1.2489605 0.7689500
##  [239,] 1.1861024 1.7670872
##  [240,] 0.9127739 1.1262892
##  [241,] 0.9718655 0.8954053
##  [242,] 0.8962517 1.1927010
##  [243,] 1.1210724 1.0293753
##  [244,] 1.0186864 1.0300678
##  [245,] 1.3508999 0.9205401
##  [246,] 0.9360839 1.0324331
##  [247,] 0.9454118 1.2493363
##  [248,] 0.8462073 0.8879132
##  [249,] 0.9296855 0.8771342
##  [250,] 0.8634529 0.9119721
##  [251,] 0.8180891 1.0694144
##  [252,] 0.7911812 1.1082801
##  [253,] 1.1616582 1.1940241
##  [254,] 0.9915413 0.8847789
##  [255,] 1.0317164 0.9826199
##  [256,] 0.8168796 0.8503762
##  [257,] 0.9351618 0.8480762
##  [258,] 0.9880451 0.9116198
##  [259,] 1.0663113 0.9976555
##  [260,] 0.9176870 1.3451447
##  [261,] 1.2399519 1.2392268
##  [262,] 1.0060880 0.8776186
##  [263,] 1.1463907 0.9628819
##  [264,] 1.3729702 1.1997167
##  [265,] 0.8550984 0.9023318
##  [266,] 0.9493941 0.8564136
##  [267,] 0.8291360 1.2709573
##  [268,] 1.1199211 1.0767553
##  [269,] 1.0688121 1.0833522
##  [270,] 1.1808651 1.2349104
##  [271,] 1.1639742 0.8553593
##  [272,] 1.0260881 1.1578823
##  [273,] 1.2055489 1.1603130
##  [274,] 0.8785564 0.9859631
##  [275,] 0.9252167 0.8820381
##  [276,] 0.8405549 0.9165234
##  [277,] 1.0084241 0.8910140
##  [278,] 0.9184574 1.2088233
##  [279,] 0.8863328 0.8569929
##  [280,] 0.8651371 1.0581083
##  [281,] 1.1937645 1.2777902
##  [282,] 1.0524278 1.1891937
##  [283,] 0.8482905 0.7000076
##  [284,] 0.9001521 0.9857275
##  [285,] 0.9890211 0.9018613
##  [286,] 0.8076705 0.7026646
##  [287,] 1.0272877 1.1314553
##  [288,] 1.0017008 1.0179948
##  [289,] 0.9751587 1.0204795
##  [290,] 0.9951927 1.2460441
##  [291,] 0.9967365 1.0809937
##  [292,] 1.3921859 1.1480222
##  [293,] 1.1168703 1.0191542
##  [294,] 1.3251006 0.9330490
##  [295,] 0.9238942 0.9761967
##  [296,] 0.9475890 1.0645299
##  [297,] 0.8621190 0.9880179
##  [298,] 0.9763378 1.1217518
##  [299,] 0.8524938 0.8573144
##  [300,] 0.7427235 0.7888599
##  [301,] 0.9041208 0.8676603
##  [302,] 0.8529047 0.9682133
##  [303,] 1.0007891 1.1878069
##  [304,] 0.9741420 0.9709770
##  [305,] 0.8728055 0.8329856
##  [306,] 0.9480186 0.7917997
##  [307,] 1.0105254 1.1569782
##  [308,] 1.0611893 0.9981138
##  [309,] 0.7708199 0.6233211
##  [310,] 0.7428875 0.7174601
##  [311,] 1.0751558 1.2876721
##  [312,] 1.0375486 1.1893969
##  [313,] 0.8922240 1.0738888
##  [314,] 0.9446686 1.2198720
##  [315,] 0.8092949 1.0171676
##  [316,] 0.9803403 1.3311444
##  [317,] 1.0077777 1.1254429
##  [318,] 0.9916693 1.2737319
##  [319,] 0.7754984 0.8558117
##  [320,] 1.2057261 1.7430890
##  [321,] 0.9250789 1.2079512
##  [322,] 0.9463195 1.0321621
##  [323,] 1.0080048 1.1257891
##  [324,] 0.9746142 1.0470514
##  [325,] 0.8261137 1.0149974
##  [326,] 1.2136525 0.8931304
##  [327,] 1.1144541 1.1031061
##  [328,] 0.8888282 0.6924013
##  [329,] 0.8798143 0.9740271
##  [330,] 1.1576085 0.9701269
##  [331,] 0.7546442 0.9209153
##  [332,] 1.1787194 1.2878420
##  [333,] 1.2302020 1.1554900
##  [334,] 0.9472861 1.2740448
##  [335,] 1.1505387 1.6716664
##  [336,] 0.8253657 0.8180606
##  [337,] 1.3806115 0.9233959
##  [338,] 1.1316007 1.1505848
##  [339,] 1.2269568 0.7744473
##  [340,] 0.9583078 0.9305569
##  [341,] 0.9334629 1.0226110
##  [342,] 0.9235831 0.9804556
##  [343,] 1.1905077 0.9589890
##  [344,] 0.8842472 1.0359307
##  [345,] 0.9439784 1.1544557
##  [346,] 1.0879927 1.0730570
##  [347,] 1.1449022 0.7332432
##  [348,] 1.1048502 1.2901438
##  [349,] 0.8727622 0.9746256
##  [350,] 0.9831825 0.8135057
##  [351,] 0.7459438 1.0218761
##  [352,] 0.9725103 0.9206295
##  [353,] 1.3969209 0.9711060
##  [354,] 0.8895518 0.7199623
##  [355,] 0.8321088 0.7938412
##  [356,] 1.0015908 0.9223267
##  [357,] 1.1088094 1.1645725
##  [358,] 0.9647698 1.1910636
##  [359,] 0.9151823 1.0519044
##  [360,] 0.8467949 0.8525618
##  [361,] 0.9375881 0.8286475
##  [362,] 1.0759430 1.1854977
##  [363,] 0.8354431 0.8825938
##  [364,] 1.2203555 1.5745379
##  [365,] 0.9077643 0.9747892
##  [366,] 1.1558771 1.0863934
##  [367,] 0.8459257 0.8199661
##  [368,] 0.9240758 1.1504240
##  [369,] 1.3441147 1.1703953
##  [370,] 1.0360751 1.0135145
##  [371,] 0.9661962 0.9259997
##  [372,] 0.8903508 1.0211540
##  [373,] 1.0020499 0.9961410
##  [374,] 0.8334780 1.0385235
##  [375,] 0.8786680 0.9685488
##  [376,] 0.9840099 0.9017707
##  [377,] 0.8194386 0.7571217
##  [378,] 1.2860489 0.9153718
##  [379,] 1.0702213 1.1294028
##  [380,] 1.2347931 1.1177022
##  [381,] 0.8892962 0.7724243
##  [382,] 1.1522986 1.2488344
##  [383,] 0.9068692 0.9296854
##  [384,] 0.8018364 0.5475286
##  [385,] 1.0570498 0.9362962
##  [386,] 1.2248845 0.8679932
##  [387,] 0.8858413 0.8012275
##  [388,] 1.1374236 0.9327977
##  [389,] 0.8964508 0.8574513
##  [390,] 0.9137466 1.1509275
##  [391,] 1.0235198 0.9268868
##  [392,] 0.8324792 0.7728359
##  [393,] 1.1582068 1.0911831
##  [394,] 0.9643542 1.2392439
##  [395,] 1.0562611 0.9925274
##  [396,] 1.1188405 1.6045315
##  [397,] 1.0498572 1.2648459
##  [398,] 0.9792722 1.0620945
##  [399,] 0.8725656 0.9095487
##  [400,] 0.7970539 0.8095415
##  [401,] 1.0747833 1.3946270
##  [402,] 0.7297564 0.7304721
##  [403,] 1.2290197 1.1157534
##  [404,] 0.8766704 0.8659397
##  [405,] 1.0801498 1.0414327
##  [406,] 0.8372594 1.1277636
##  [407,] 1.1460712 0.8586834
##  [408,] 0.8495929 1.0981643
##  [409,] 0.9754059 1.1521310
##  [410,] 0.9765771 1.1037708
##  [411,] 0.8197834 0.9915042
##  [412,] 1.1691479 1.1226246
##  [413,] 0.9369109 1.1337906
##  [414,] 0.8597371 0.8676621
##  [415,] 0.9705766 0.9612007
##  [416,] 1.4053453 1.0180953
##  [417,] 1.2523909 0.7751819
##  [418,] 0.8506695 0.9741106
##  [419,] 0.8894310 0.7643555
##  [420,] 0.8014763 0.8986224
##  [421,] 1.1836242 1.4119073
##  [422,] 0.9752538 1.1024748
##  [423,] 0.6801935 0.7539376
##  [424,] 0.9424596 0.7227529
##  [425,] 0.9255313 0.9668788
##  [426,] 0.9645816 1.0735946
##  [427,] 0.8999194 1.1169306
##  [428,] 0.8895277 0.9990581
##  [429,] 1.0799638 1.2663881
##  [430,] 0.8903421 1.2227805
##  [431,] 1.1838503 0.8624211
##  [432,] 1.0241618 0.7148077
##  [433,] 0.7513974 0.6082582
##  [434,] 0.7317589 0.6415362
##  [435,] 1.3245109 1.0606714
##  [436,] 1.2216764 0.8797724
##  [437,] 1.0265948 1.0352610
##  [438,] 1.4304778 1.4371210
##  [439,] 0.8676367 0.8415598
##  [440,] 0.9423997 0.9251813
##  [441,] 0.9003205 1.2499417
##  [442,] 1.0807169 1.0178577
##  [443,] 1.3292683 1.1561414
##  [444,] 1.3926232 1.1605922
##  [445,] 0.8285270 0.6731463
##  [446,] 1.4418486 1.0835018
##  [447,] 0.8823850 1.0599423
##  [448,] 1.0397504 1.4165172
##  [449,] 0.9215346 0.8278973
##  [450,] 0.8282402 0.7610899
##  [451,] 1.2029547 0.9066483
##  [452,] 0.7903844 0.9205069
##  [453,] 0.9333425 1.0764580
##  [454,] 1.0457651 0.9897145
##  [455,] 0.9543424 0.7901951
##  [456,] 0.8865136 0.7652450
##  [457,] 1.4130877 0.8191948
##  [458,] 1.0587422 1.4549422
##  [459,] 1.1053935 1.3506427
##  [460,] 1.4252054 1.1272527
##  [461,] 1.1157218 0.9309311
##  [462,] 0.8714748 1.2351497
##  [463,] 1.0155231 1.2716701
##  [464,] 0.6325484 0.7478080
##  [465,] 0.7950897 0.9562251
##  [466,] 0.9365994 0.7625336
##  [467,] 0.7955654 0.7950480
##  [468,] 1.1724451 1.2201076
##  [469,] 0.8683845 0.7636876
##  [470,] 1.0500869 0.9050799
##  [471,] 1.2996655 0.9409456
##  [472,] 0.8813081 0.9376525
##  [473,] 0.8427893 0.7630633
##  [474,] 1.0196749 1.3149329
##  [475,] 0.7742453 0.8377224
##  [476,] 0.8500018 0.9442572
##  [477,] 1.3313780 0.9964611
##  [478,] 1.0063918 0.9853679
##  [479,] 0.9712210 0.8790528
##  [480,] 0.8445767 0.9722997
##  [481,] 0.9040359 1.2003595
##  [482,] 1.0885465 1.0843326
##  [483,] 1.0586603 1.3712891
##  [484,] 0.7845068 0.7702120
##  [485,] 1.1745150 0.9997497
##  [486,] 0.7671113 0.6062066
##  [487,] 0.8304969 0.7641393
##  [488,] 0.9068432 0.8484416
##  [489,] 1.1753427 1.6146487
##  [490,] 0.8988281 0.8410159
##  [491,] 1.1441597 0.9568104
##  [492,] 1.0684456 1.5833211
##  [493,] 1.1098285 1.3812828
##  [494,] 1.2072453 0.9935604
##  [495,] 0.8473175 0.8436364
##  [496,] 0.7021817 0.8598793
##  [497,] 1.1376871 0.7424985
##  [498,] 0.9550483 0.8961786
##  [499,] 1.1473994 0.9418396
##  [500,] 1.0086437 1.0927511
##  [501,] 1.1490560 1.0252202
##  [502,] 1.5574506 1.2411536
##  [503,] 0.9581001 1.3414987
##  [504,] 0.9845085 1.0546680
##  [505,] 0.8738809 0.8574317
##  [506,] 1.0112366 0.9300961
##  [507,] 1.4837579 1.0885127
##  [508,] 0.8445136 0.6571927
##  [509,] 1.1178864 1.5858061
##  [510,] 0.8912999 1.0721140
##  [511,] 0.8980365 0.9656792
##  [512,] 0.9909082 1.1543668
##  [513,] 1.0272688 1.1278770
##  [514,] 1.0399699 0.9225417
##  [515,] 1.1121755 0.7010774
##  [516,] 0.8348275 0.7548632
##  [517,] 1.0216971 1.1624283
##  [518,] 0.8313080 0.6689749
##  [519,] 0.8463715 0.9025559
##  [520,] 0.7658396 0.8453400
##  [521,] 1.2463817 1.3916427
##  [522,] 1.0278436 1.0592491
##  [523,] 1.1452459 1.0485977
##  [524,] 1.2287295 0.9137085
##  [525,] 0.8966078 0.9842245
##  [526,] 0.9165931 0.8811141
##  [527,] 0.9397029 0.8332270
##  [528,] 1.1542833 1.0940622
##  [529,] 0.8022357 0.9423040
##  [530,] 1.5175200 1.2190898
##  [531,] 1.3475730 0.9867358
##  [532,] 1.2311168 0.8686386
##  [533,] 1.2519115 1.1796833
##  [534,] 1.1159308 0.9117076
##  [535,] 1.0354753 0.8684263
##  [536,] 0.7894890 0.8882576
##  [537,] 0.8323596 0.7197817
##  [538,] 0.8308104 0.9230633
##  [539,] 1.0809198 1.0561457
##  [540,] 1.0523646 0.7926060
##  [541,] 1.0447104 0.8450984
##  [542,] 0.9868576 1.1779152
##  [543,] 0.9798174 0.9635113
##  [544,] 1.0353471 1.3637164
##  [545,] 1.0147097 1.0059097
##  [546,] 0.8893333 0.9344103
##  [547,] 0.8914267 0.8507094
##  [548,] 1.0236566 1.0971820
##  [549,] 1.0952677 1.2494209
##  [550,] 0.9506815 0.9531032
##  [551,] 0.9911316 0.7054064
##  [552,] 1.4967367 0.9831982
##  [553,] 1.2702762 0.8953689
##  [554,] 1.3421103 0.9766552
##  [555,] 1.1660790 1.0520501
##  [556,] 0.9133143 1.0009531
##  [557,] 1.0594743 1.2216997
##  [558,] 0.9237289 1.0111019
##  [559,] 0.9083371 0.8910060
##  [560,] 0.7148161 0.7474786
##  [561,] 0.7516922 0.8775841
##  [562,] 0.9312675 0.9453406
##  [563,] 1.1803352 0.9895299
##  [564,] 0.9974212 0.9112288
##  [565,] 0.9942754 1.1214666
##  [566,] 1.0480474 0.7088059
##  [567,] 0.6575838 0.7167451
##  [568,] 0.9608140 1.0570694
##  [569,] 1.0787269 0.8109374
##  [570,] 1.3769576 1.1619868
##  [571,] 0.9959614 1.0519974
##  [572,] 0.8927101 0.8818347
##  [573,] 0.7973760 0.8632731
##  [574,] 0.8781703 1.1212683
##  [575,] 1.0221562 0.9107141
##  [576,] 1.5060247 0.9986078
##  [577,] 1.2892990 1.1426447
##  [578,] 0.9082846 0.8749597
##  [579,] 1.2144048 1.1915012
##  [580,] 1.1148657 1.4018671
##  [581,] 1.1798356 1.0108688
##  [582,] 0.8237682 0.9145178
##  [583,] 1.0421321 1.1284264
##  [584,] 1.0656758 0.6921696
##  [585,] 1.0658346 1.0136370
##  [586,] 0.8843183 0.8696266
##  [587,] 0.9642890 0.9933635
##  [588,] 1.1279147 1.3704610
##  [589,] 1.0820833 1.2484599
##  [590,] 0.8589963 0.8104763
##  [591,] 1.0240141 0.7267184
##  [592,] 0.9143472 0.9552111
##  [593,] 0.8828614 0.7934863
##  [594,] 1.2907187 0.9608899
##  [595,] 1.3709075 1.0061389
##  [596,] 1.0243512 1.1497412
##  [597,] 1.4971976 0.9911098
##  [598,] 1.1450677 1.0390080
##  [599,] 1.0300643 1.1271092
##  [600,] 1.0684456 1.5833211
##  [601,] 0.9595367 0.9759528
##  [602,] 1.1504045 0.9098073
##  [603,] 1.1490591 0.7214711
##  [604,] 0.8074763 0.8319270
##  [605,] 1.0126300 0.8465653
##  [606,] 1.1387590 0.9395431
##  [607,] 0.9903471 1.0677796
##  [608,] 0.7378982 0.6886856
##  [609,] 0.9662773 0.8740253
##  [610,] 0.9608148 0.9899144
##  [611,] 1.2272885 1.3440858
##  [612,] 0.8763618 1.0896068
##  [613,] 1.1931403 1.2683514
##  [614,] 0.9250789 1.2079512
##  [615,] 0.7669429 0.7794204
##  [616,] 0.9171175 1.0160921
##  [617,] 0.7433968 0.7596221
##  [618,] 0.8229206 1.0630548
##  [619,] 0.8364976 1.0050405
##  [620,] 0.9595456 1.2710908
##  [621,] 0.9934958 0.7687502
##  [622,] 0.7994724 1.0247581
##  [623,] 0.7902883 1.0516512
##  [624,] 0.8466349 1.1210834
##  [625,] 1.0182304 1.0038691
##  [626,] 0.8258651 0.7242357
##  [627,] 1.4015697 1.1429499
##  [628,] 1.3178685 1.2938181
##  [629,] 1.0632873 0.9277636
##  [630,] 0.8271498 0.7739249
##  [631,] 0.8553083 0.9428036
##  [632,] 1.0599927 1.1230428
##  [633,] 1.3059745 1.2864307
##  [634,] 1.2074015 1.1244135
##  [635,] 0.8051013 1.0465159
##  [636,] 0.8063736 0.9069611
##  [637,] 0.9818285 1.0800002
##  [638,] 0.9452951 1.1032836
##  [639,] 0.9406708 1.0071179
##  [640,] 1.3284354 0.9656821
##  [641,] 0.7346117 0.6603188
##  [642,] 1.2441205 1.1942798
##  [643,] 1.2009845 0.7892181
##  [644,] 0.8646547 1.0223850
##  [645,] 1.3073631 1.5722734
##  [646,] 1.0250009 0.9031840
##  [647,] 0.9536624 1.1783853
##  [648,] 1.1862750 1.0420095
##  [649,] 0.7575625 0.8136649
##  [650,] 0.9029284 0.9866084
##  [651,] 0.8457157 0.9423593
##  [652,] 1.1863245 1.4661013
##  [653,] 0.9624093 1.1237651
##  [654,] 0.9957217 1.0949456
##  [655,] 1.0482231 0.8476525
##  [656,] 0.8741259 0.8524817
##  [657,] 0.9555552 0.9290137
##  [658,] 0.7568346 0.6981095
##  [659,] 0.8882204 0.9400896
##  [660,] 1.3206111 0.8180770
##  [661,] 1.2031149 1.2360162
##  [662,] 0.8555527 1.0054115
##  [663,] 1.2195857 1.1349313
##  [664,] 0.8199159 0.7748973
##  [665,] 0.9671718 1.4218091
##  [666,] 0.9312522 0.8653532
##  [667,] 0.7151748 0.7923122
##  [668,] 1.6535752 1.1589891
##  [669,] 1.1760195 1.2364990
##  [670,] 1.0623395 1.0122073
##  [671,] 1.4994530 1.0473084
##  [672,] 1.4706016 1.0882971
##  [673,] 1.0745438 1.3377947
##  [674,] 1.1317734 1.3334728
##  [675,] 0.9471077 0.9817540
##  [676,] 0.8100260 0.6953008
##  [677,] 0.8234994 0.8157302
##  [678,] 0.9670493 0.9011264
##  [679,] 1.1259783 0.9966803
##  [680,] 0.8008615 0.8636226
##  [681,] 1.0823943 1.2344912
##  [682,] 0.9090052 0.7729449
##  [683,] 0.8798376 1.1528359
##  [684,] 0.8189700 0.7906862
##  [685,] 0.7827280 0.7279962
##  [686,] 0.8832458 1.0253818
##  [687,] 0.7887463 0.8781827
##  [688,] 0.9867923 0.8472245
##  [689,] 1.0634514 1.1771721
##  [690,] 0.9479929 1.3316592
##  [691,] 1.6384480 1.1346643
##  [692,] 1.1937805 1.4244644
##  [693,] 0.8578407 1.1258747
##  [694,] 0.8395580 0.7753374
##  [695,] 0.8579800 0.8515674
##  [696,] 0.8929692 0.9049618
##  [697,] 1.2335510 0.9361922
##  [698,] 1.4664905 0.9660881
##  [699,] 0.9031006 1.0869686
##  [700,] 0.8478174 0.8809320
##  [701,] 0.8697306 0.7729489
##  [702,] 0.8115761 0.8421545
##  [703,] 1.2015233 1.4340160
##  [704,] 1.0624816 0.7826133
##  [705,] 1.0912161 0.8508818
##  [706,] 0.9646415 1.0198309
##  [707,] 1.0116670 0.8754954
##  [708,] 0.9939614 1.2373074
##  [709,] 0.9618637 0.9487582
##  [710,] 1.0138454 1.0037753
##  [711,] 1.1124341 1.3124262
##  [712,] 0.8405409 0.8564074
##  [713,] 0.8765413 0.8060267
##  [714,] 1.0313662 0.8644737
##  [715,] 1.1294820 1.1625088
##  [716,] 0.8212777 0.7053702
##  [717,] 0.7425487 0.7883213
##  [718,] 0.8823233 0.8481776
##  [719,] 1.2184049 1.4305914
##  [720,] 1.1090313 1.2839713
##  [721,] 1.0794271 1.3412266
##  [722,] 1.3803572 0.9229496
##  [723,] 1.1307897 1.2214085
##  [724,] 0.6655254 0.6481444
##  [725,] 1.2223288 0.9284394
##  [726,] 1.0688501 1.0719749
##  [727,] 1.3030634 0.9155104
##  [728,] 0.9573354 1.1404477
##  [729,] 1.1004579 0.8973787
##  [730,] 0.9295955 0.9600667
##  [731,] 0.9992659 0.9005569
##  [732,] 1.6378827 1.3531655
##  [733,] 0.8201965 1.1130748
##  [734,] 1.1873336 1.2395266
##  [735,] 0.9774942 0.8531910
##  [736,] 0.8796710 0.8739049
##  [737,] 1.4744257 1.1661782
##  [738,] 0.9994928 1.1174150
##  [739,] 0.9355978 0.9449902
##  [740,] 1.0949576 1.2874203
##  [741,] 1.3768718 1.1463446
##  [742,] 0.8576736 0.7786925
##  [743,] 1.2168519 1.1509463
##  [744,] 1.2310825 1.2653594
##  [745,] 0.9554596 1.1100078
##  [746,] 0.9785907 1.1277205
##  [747,] 0.6965759 0.7483674
##  [748,] 0.7239748 0.7747933
##  [749,] 0.9418582 0.9458627
##  [750,] 1.4252516 1.0772577
##  [751,] 1.0680822 1.1749805
##  [752,] 0.8653809 1.2208553
##  [753,] 1.0820179 1.1544577
##  [754,] 1.0465571 1.1473167
##  [755,] 0.8850585 1.1140585
##  [756,] 1.0342309 1.1431133
##  [757,] 1.0000553 1.0421328
##  [758,] 0.9646298 0.7470418
##  [759,] 0.9653770 0.8915016
##  [760,] 1.0377525 1.1104371
##  [761,] 0.8016455 0.6750751
##  [762,] 0.8674828 0.9319376
##  [763,] 0.8612449 0.8308213
##  [764,] 1.0888787 1.0655153
##  [765,] 0.8800079 1.1003809
##  [766,] 1.2497405 1.4192221
##  [767,] 0.7840666 0.8992395
##  [768,] 0.8462230 0.9803345
##  [769,] 1.0248362 1.0327393
##  [770,] 0.7715156 0.6624294
##  [771,] 1.0144749 0.9700797
##  [772,] 0.9565803 0.9213343
##  [773,] 0.9428110 1.0316421
##  [774,] 1.1203155 1.1538110
##  [775,] 0.8923684 0.6572675
##  [776,] 1.1979562 1.2437542
##  [777,] 1.1583102 1.4077319
##  [778,] 0.9787929 0.6809072
##  [779,] 1.2154446 1.5336866
##  [780,] 1.1812711 1.2798867
##  [781,] 1.1213434 0.9964038
##  [782,] 0.9393269 1.0988165
##  [783,] 1.2261197 1.0083325
##  [784,] 0.8017994 0.8319754
##  [785,] 0.9176935 1.2899664
##  [786,] 0.8268666 0.7842430
##  [787,] 0.7652806 0.8697081
##  [788,] 0.7415575 0.8382894
##  [789,] 0.9695706 0.8582070
##  [790,] 1.0979369 1.5765650
##  [791,] 0.7600740 0.7703445
##  [792,] 1.2049876 1.2625119
##  [793,] 0.8237252 0.8027471
##  [794,] 1.5406221 1.1074253
##  [795,] 1.1292038 0.8228513
##  [796,] 1.2186348 1.1531901
##  [797,] 0.9380929 0.9515107
##  [798,] 1.0383300 1.2591996
##  [799,] 1.2111358 1.1855037
##  [800,] 0.8672311 1.0357544
##  [801,] 0.9005554 0.9644369
##  [802,] 0.9341105 1.1480250
##  [803,] 1.3725052 1.0294293
##  [804,] 0.8982613 1.0182232
##  [805,] 1.1144322 1.2719436
##  [806,] 1.0641924 1.1108379
##  [807,] 0.8907793 1.0811199
##  [808,] 1.4490413 1.1202655
##  [809,] 1.1118995 1.1223700
##  [810,] 0.9829853 0.8046756
##  [811,] 1.0204120 0.8182327
##  [812,] 0.8206759 0.9749901
##  [813,] 1.0239948 1.0005067
##  [814,] 0.8413453 0.9895975
##  [815,] 1.0432933 1.2018050
##  [816,] 1.1300556 0.7716239
##  [817,] 0.8067842 0.8274120
##  [818,] 0.8878230 1.0849221
##  [819,] 1.1234790 1.1207520
##  [820,] 1.0190822 0.9873752
##  [821,] 0.9517965 1.1219984
##  [822,] 0.9022431 0.8749648
##  [823,] 0.6092081 0.6648664
##  [824,] 0.8504473 0.8094771
##  [825,] 1.0586349 1.2074618
##  [826,] 0.9014688 0.9970189
##  [827,] 0.8290326 0.7731403
##  [828,] 1.0204077 0.9013266
##  [829,] 0.9150651 0.9434033
##  [830,] 0.9872483 1.0018633
##  [831,] 0.8808159 1.0238260
##  [832,] 1.0423452 1.0085540
##  [833,] 1.3995883 1.1737465
##  [834,] 0.8766723 1.0285116
##  [835,] 1.1475700 1.4491169
##  [836,] 0.9608148 0.9899144
##  [837,] 0.9434018 1.0407879
##  [838,] 1.2474747 1.0860268
##  [839,] 0.9517166 1.2540850
##  [840,] 0.9178702 0.8726524
##  [841,] 0.9075137 0.8069502
##  [842,] 0.9174268 0.9715202
##  [843,] 0.7627844 0.8185525
##  [844,] 0.9698816 1.1750516
##  [845,] 0.8896662 1.1325824
##  [846,] 0.7940434 0.6109937
##  [847,] 0.7131411 0.7578657
##  [848,] 0.9795560 1.0390760
##  [849,] 0.9083493 0.8643287
##  [850,] 1.0759322 1.0105926
##  [851,] 1.0681861 1.0790214
##  [852,] 1.0614957 1.3971435
##  [853,] 1.0017547 0.8471292
##  [854,] 0.8154865 0.9908245
##  [855,] 1.0799074 1.4168149
##  [856,] 1.1743886 0.7571724
##  [857,] 1.0923075 0.9860912
##  [858,] 0.8298103 0.6463813
##  [859,] 1.0861248 1.2981766
##  [860,] 1.1054961 1.0191428
##  [861,] 1.2489605 0.7689500
##  [862,] 1.0294195 0.8521888
##  [863,] 0.9563037 1.2423652
##  [864,] 0.9872489 1.1295691
##  [865,] 0.8677390 1.2746663
##  [866,] 1.2366070 1.0362699
##  [867,] 1.0179758 1.1332495
##  [868,] 0.8699113 0.8036200
##  [869,] 1.0023747 1.0928934
##  [870,] 1.1787194 1.2878420
##  [871,] 0.9009150 0.7148741
##  [872,] 0.9662284 1.2119608
##  [873,] 0.7576574 0.5956411
##  [874,] 0.9451158 1.0164001
##  [875,] 0.8484433 0.9475326
##  [876,] 1.1159475 0.9865096
##  [877,] 1.1477535 1.1072671
##  [878,] 0.9797820 0.9334428
##  [879,] 1.0596272 1.2451275
##  [880,] 1.0006308 0.9949544
##  [881,] 1.0750511 1.1383153
##  [882,] 1.0821003 0.8697494
##  [883,] 1.0827986 0.8043594
##  [884,] 1.0861175 1.0405458
##  [885,] 1.2789809 1.1695270
##  [886,] 1.0350200 0.9007766
##  [887,] 0.8519192 0.7626937
##  [888,] 0.9048148 0.9550008
##  [889,] 1.0023264 0.9589547
##  [890,] 1.1780625 0.9765703
##  [891,] 1.0902553 1.2949207
##  [892,] 0.7657973 0.7874400
##  [893,] 1.3294215 1.0340341
##  [894,] 1.0260881 1.1578823
##  [895,] 0.7895384 0.9478399
##  [896,] 0.9407229 0.7660504
##  [897,] 0.7549322 0.6540444
##  [898,] 0.9536874 0.7174174
##  [899,] 0.8818741 0.7075909
##  [900,] 1.5574506 1.2411536
##  [901,] 1.0658620 0.9752208
##  [902,] 0.8636265 0.9788958
##  [903,] 1.0585484 1.3743246
##  [904,] 0.6812832 0.6515131
##  [905,] 0.8702257 0.7451173
##  [906,] 1.0754237 1.1888221
##  [907,] 1.0366852 1.0013293
##  [908,] 0.9995342 0.8621306
##  [909,] 1.0971440 0.8626939
##  [910,] 1.4232627 0.9838266
##  [911,] 1.5899531 0.9865231
##  [912,] 1.5098554 1.4818775
##  [913,] 0.7706932 0.6550986
##  [914,] 0.8564592 0.8536594
##  [915,] 1.3106809 0.9983124
##  [916,] 1.0465379 0.7568226
##  [917,] 1.1423161 1.0975263
##  [918,] 1.2599220 0.7849282
##  [919,] 0.7856900 0.9630662
##  [920,] 0.8241907 0.9788052
##  [921,] 1.0287310 0.8127956
##  [922,] 0.8179429 1.0435796
##  [923,] 0.8371532 1.1011680
##  [924,] 1.0635863 0.9632901
##  [925,] 0.9306157 0.9059904
##  [926,] 1.0032030 0.9484892
##  [927,] 1.1014398 0.8593719
##  [928,] 0.8005289 0.7468838
##  [929,] 1.0466191 0.7786806
##  [930,] 0.8510820 0.8473879
##  [931,] 1.2118481 1.1243905
##  [932,] 0.8639700 1.1023001
##  [933,] 1.1017669 1.2180802
##  [934,] 1.1055281 1.4552279
##  [935,] 0.8468172 1.0313703
##  [936,] 0.9902147 0.8304899
##  [937,] 1.0442251 0.7630756
##  [938,] 1.0521504 0.8903210
##  [939,] 0.9750702 1.0023183
##  [940,] 0.8545725 1.0310564
##  [941,] 0.9624693 1.3682759
##  [942,] 1.2367676 0.9562908
##  [943,] 1.1505387 1.6716664
##  [944,] 1.1551780 0.8774259
##  [945,] 1.0482274 1.1580854
##  [946,] 1.0342309 1.1431133
##  [947,] 0.8670308 1.1085177
##  [948,] 1.0743067 1.3848268
##  [949,] 1.0284775 0.8335853
##  [950,] 0.7882144 0.8225803
##  [951,] 0.8871574 0.8002286
##  [952,] 0.9149705 0.9762213
##  [953,] 0.8613754 0.9548972
##  [954,] 1.0135309 1.0165374
##  [955,] 0.9978359 0.8521817
##  [956,] 1.0573904 0.9046225
##  [957,] 1.3279451 1.3664765
##  [958,] 0.8661492 1.0611010
##  [959,] 1.1284609 0.9583839
##  [960,] 1.4883526 1.3065972
##  [961,] 1.0274615 1.0265689
##  [962,] 0.7720150 0.8867931
##  [963,] 1.1503312 1.0839691
##  [964,] 1.4245235 1.8323839
##  [965,] 1.0411764 1.0005472
##  [966,] 1.6378827 1.3531655
##  [967,] 1.1091143 1.0959738
##  [968,] 1.3093758 1.2619024
##  [969,] 0.9950083 1.2953977
##  [970,] 1.0967705 0.9537356
##  [971,] 0.8741003 0.7551137
##  [972,] 1.2341962 1.2495527
##  [973,] 0.7519871 0.8213421
##  [974,] 0.9856428 1.4435310
##  [975,] 1.0687806 0.9760687
##  [976,] 0.7570545 1.0615182
##  [977,] 1.1865500 1.4513231
##  [978,] 1.0969054 1.3440649
##  [979,] 1.0112667 0.9322210
##  [980,] 1.1896496 1.3656749
##  [981,] 1.0670192 1.3469078
##  [982,] 1.0794982 0.7379870
##  [983,] 0.8482538 0.8468465
##  [984,] 1.3173230 0.9652958
##  [985,] 1.6535752 1.1589891
##  [986,] 1.0141425 0.8891776
##  [987,] 0.9719172 0.9174639
##  [988,] 1.0011654 0.6959260
##  [989,] 0.7651313 0.8095276
##  [990,] 1.0749398 1.3810250
##  [991,] 1.2050766 1.2519167
##  [992,] 1.6400472 1.0898700
##  [993,] 1.2311581 0.8606657
##  [994,] 1.2479200 0.9792822
##  [995,] 0.8430941 0.9036120
##  [996,] 1.1293111 1.6756912
##  [997,] 0.7675634 0.7406246
##  [998,] 0.9813924 1.1521366
##  [999,] 1.0201591 1.2959360
## 
## $model.matrix
##   (Intercept) treatment1 treatment2 treatment3 mesocosm1 mesocosm2
## 1           1         -1         -1         -1        -1        -1
## 2           1          1          0          0        -1        -1
## 3           1         -1         -1         -1         1         0
## 4           1          0          1          0         0         1
## 5           1          0          1          0        -1        -1
## 6           1         -1         -1         -1        -1        -1
## 7           1          0          0          1        -1        -1
## 8           1          0          0          1         0         1
## 9           1          1          0          0         1         0
## 
## $terms
## dist_tab_assay ~ treatment + mesocosm
## attr(,"variables")
## list(dist_tab_assay, treatment, mesocosm)
## attr(,"factors")
##                treatment mesocosm
## dist_tab_assay         0        0
## treatment              1        0
## mesocosm               0        1
## attr(,"term.labels")
## [1] "treatment" "mesocosm" 
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## 
## attr(,"class")
## [1] "adonis"
anova(betadisper(dist_tab_assay,samplesHedophylum$treatment))
## Analysis of Variance Table
## 
## Response: Distances
##           Df Sum Sq Mean Sq F value Pr(>F)
## Groups     3 1654.4  551.46  1.6828 0.2847
## Residuals  5 1638.5  327.70
anova(betadisper(dist_tab_assay,samplesHedophylum$mesocosm))
## Analysis of Variance Table
## 
## Response: Distances
##           Df Sum Sq Mean Sq F value Pr(>F)
## Groups     2 2115.1 1057.55  2.3219 0.1791
## Residuals  6 2732.8  455.47
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8
## 
## attached base packages:
## [1] grid      stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] EnhancedVolcano_1.12.0      ashr_2.2-54                
##  [3] apeglm_1.16.0               tximport_1.22.0            
##  [5] ggvenn_0.1.10               limma_3.50.3               
##  [7] dplyr_1.1.1                 vegan_2.6-4                
##  [9] lattice_0.20-45             permute_0.9-7              
## [11] gplots_3.1.3                genefilter_1.76.0          
## [13] RColorBrewer_1.1-3          pheatmap_1.0.12            
## [15] markdown_1.5                ggrepel_0.9.3              
## [17] ggplot2_3.4.2               BiocManager_1.30.20        
## [19] devtools_2.4.5              usethis_2.1.6              
## [21] vsn_3.62.0                  adegenet_2.1.10            
## [23] ade4_1.7-22                 DESeq2_1.34.0              
## [25] SummarizedExperiment_1.24.0 Biobase_2.54.0             
## [27] MatrixGenerics_1.6.0        matrixStats_0.63.0         
## [29] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
## [31] IRanges_2.28.0              S4Vectors_0.32.4           
## [33] BiocGenerics_0.40.0        
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.8             igraph_1.4.1           splines_4.1.2         
##   [4] BiocParallel_1.28.3    digest_0.6.31          invgamma_1.1          
##   [7] htmltools_0.5.5        SQUAREM_2021.1         fansi_1.0.4           
##  [10] magrittr_2.0.3         memoise_2.0.1          cluster_2.1.4         
##  [13] tzdb_0.3.0             remotes_2.4.2          readr_2.1.4           
##  [16] Biostrings_2.62.0      annotate_1.72.0        extrafont_0.19        
##  [19] vroom_1.6.1            extrafontdb_1.0        bdsmatrix_1.3-6       
##  [22] prettyunits_1.1.1      colorspace_2.1-0       blob_1.2.4            
##  [25] xfun_0.38              hexbin_1.28.3          callr_3.7.3           
##  [28] crayon_1.5.2           RCurl_1.98-1.10        jsonlite_1.8.4        
##  [31] survival_3.5-5         ape_5.7-1              glue_1.6.2            
##  [34] gtable_0.3.3           zlibbioc_1.40.0        XVector_0.34.0        
##  [37] seqinr_4.2-23          proj4_1.0-12           DelayedArray_0.20.0   
##  [40] pkgbuild_1.4.0         Rttf2pt1_1.3.12        maps_3.4.1            
##  [43] scales_1.2.1           mvtnorm_1.1-3          DBI_1.1.3             
##  [46] miniUI_0.1.1.1         Rcpp_1.0.10            xtable_1.8-4          
##  [49] emdbook_1.3.12         bit_4.0.5              preprocessCore_1.56.0 
##  [52] truncnorm_1.0-9        profvis_0.3.7          htmlwidgets_1.6.2     
##  [55] httr_1.4.5             ellipsis_0.3.2         farver_2.1.1          
##  [58] urlchecker_1.0.1       pkgconfig_2.0.3        XML_3.99-0.14         
##  [61] sass_0.4.5             locfit_1.5-9.7         utf8_1.2.3            
##  [64] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.0           
##  [67] reshape2_1.4.4         later_1.3.0            AnnotationDbi_1.56.2  
##  [70] munsell_0.5.0          tools_4.1.2            cachem_1.0.7          
##  [73] cli_3.6.1              generics_0.1.3         RSQLite_2.3.0         
##  [76] evaluate_0.20          stringr_1.5.0          fastmap_1.1.1         
##  [79] yaml_2.3.7             processx_3.8.0         knitr_1.42            
##  [82] bit64_4.0.5            fs_1.6.1               caTools_1.18.2        
##  [85] purrr_1.0.1            KEGGREST_1.34.0        nlme_3.1-162          
##  [88] mime_0.12              ash_1.0-15             ggrastr_1.0.1         
##  [91] compiler_4.1.2         rstudioapi_0.14        beeswarm_0.4.0        
##  [94] curl_5.0.0             png_0.1-8              affyio_1.64.0         
##  [97] tibble_3.2.1           geneplotter_1.72.0     bslib_0.4.2           
## [100] stringi_1.7.12         ps_1.7.3               ggalt_0.4.0           
## [103] Matrix_1.5-1           vctrs_0.6.1            pillar_1.9.0          
## [106] lifecycle_1.0.3        jquerylib_0.1.4        irlba_2.3.5.1         
## [109] bitops_1.0-7           httpuv_1.6.9           R6_2.5.1              
## [112] affy_1.72.0            promises_1.2.0.1       KernSmooth_2.23-20    
## [115] vipor_0.4.5            sessioninfo_1.2.2      MASS_7.3-58.3         
## [118] gtools_3.9.4           pkgload_1.3.2          withr_2.5.0           
## [121] GenomeInfoDbData_1.2.7 hms_1.1.3              mgcv_1.8-42           
## [124] parallel_4.1.2         coda_0.19-4            rmarkdown_2.21        
## [127] mixsqp_0.3-48          bbmle_1.0.25           numDeriv_2016.8-1.1   
## [130] shiny_1.7.4            ggbeeswarm_0.7.1